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EfficientBioAI: Making Bioimaging AI Models Efficient in Energy, Latency and Representation (2306.06152v1)

Published 9 Jun 2023 in cs.LG and eess.IV

Abstract: AI has been widely used in bioimage image analysis nowadays, but the efficiency of AI models, like the energy consumption and latency is not ignorable due to the growing model size and complexity, as well as the fast-growing analysis needs in modern biomedical studies. Like we can compress large images for efficient storage and sharing, we can also compress the AI models for efficient applications and deployment. In this work, we present EfficientBioAI, a plug-and-play toolbox that can compress given bioimaging AI models for them to run with significantly reduced energy cost and inference time on both CPU and GPU, without compromise on accuracy. In some cases, the prediction accuracy could even increase after compression, since the compression procedure could remove redundant information in the model representation and therefore reduce over-fitting. From four different bioimage analysis applications, we observed around 2-5 times speed-up during inference and 30-80$\%$ saving in energy. Cutting the runtime of large scale bioimage analysis from days to hours or getting a two-minutes bioimaging AI model inference done in near real-time will open new doors for method development and biomedical discoveries. We hope our toolbox will facilitate resource-constrained bioimaging AI and accelerate large-scale AI-based quantitative biological studies in an eco-friendly way, as well as stimulate further research on the efficiency of bioimaging AI.

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References (28)
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Nature Methods 18 (10), 1192–1195 (2021) . (5) von Chamier, L. et al. Democratising deep learning for microscopy with ZeroCostDL4Mic. Nature Communications 12 (1), 2276 (2021) . (6) Pachitariu, M. & Stringer, C. Cellpose 2.0: how to train your own model. Nature Methods 19 (12), 1634–1641 (2022) . (7) Sonneck, J. & Chen, J. MMV_im2im: An Open Source Microscopy Machine Vision Toolbox for Image-to-Image Transformation (2023). ArXiv:2209.02498 [cs]. (8) Mahecic, D. et al. Event-driven acquisition for content-enriched microscopy. Nature Methods 19 (10), 1262–1267 (2022) . (9) Desislavov, R., Martínez-Plumed, F. & Hernández-Orallo, J. Trends in AI inference energy consumption: Beyond the performance-vs-parameter laws of deep learning. Sustainable Computing: Informatics and Systems 38, 100857 (2023) . (10) Xu, X. et al. Quantization of Fully Convolutional Networks for Accurate Biomedical Image Segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2018) . (11) Gou, J., Yu, B., Maybank, S. J. & Tao, D. Knowledge Distillation: A Survey. International Journal of Computer Vision 129 (6), 1789–1819 (2021) . (12) Ding, C. et al. REQ-YOLO: A Resource-Aware, Efficient Quantization Framework for Object Detection on FPGAs (2019). ArXiv:1909.13396 [cs]. (13) Kozlov, A., Lazarevich, I., Shamporov, V., Lyalyushkin, N. & Gorbachev, Y. Neural Network Compression Framework for fast model inference (2020). ArXiv:2002.08679 [cs, eess]. (14) Li, Y. et al. MQBench: Towards Reproducible and Deployable Model Quantization Benchmark. Neural Information Processing Systems (2021) . (15) Siddegowda, S. et al. Neural Network Quantization with AI Model Efficiency Toolkit (AIMET) (2022). ArXiv:2201.08442 [cs.LG]. (16) Microsoft. Neural Network Intelligence (2022). URL https://github.com/microsoft/nni. (17) AskariHemmat, M. et al. U-Net Fixed-Point Quantization for Medical Image Segmentation. Large-Scale Annotation of Biomedical Data and Expert Label Synthesis and Hardware Aware Learning for Medical Imaging and Computer Assisted Intervention 115–124 (2019) . (18) Micikevicius, P. et al. FP8 Formats for Deep Learning (2022). ArXiv:2209.05433 [cs]. (19) Han, S., Pool, J., Tran, J. & Dally, W. J. Learning both Weights and Connections for Efficient Neural Networks (2015). ArXiv:1506.02626 [cs] . (20) He, Y., Liu, P., Wang, Z., Hu, Z. & Yang, Y. Filter Pruning via Geometric Median for Deep Convolutional Neural Networks Acceleration (2018). ArXiv:1811.00250 [cs] . (21) Weigert, M. et al. Content-aware image restoration: pushing the limits of fluorescence microscopy. Nature Methods 15 (12), 1090–1097 (2018) . (22) Cardoso, M. J. et al. MONAI: An open-source framework for deep learning in healthcare (2022). ArXiv:2211.02701 [cs] . (23) Cardiff, R. D., Miller, C. H. & Munn, R. J. Manual Hematoxylin and Eosin Staining of Mouse Tissue Sections. Cold Spring Harbor Protocols 2014 (6), pdb.prot073411 (2014) . (24) Tomasovic, A. et al. Interference with ERK-dimerization at the nucleocytosolic interface targets pathological ERK1/2 signaling without cardiotoxic side-effects. Nature Communications 11 (1), 1733 (2020) . (25) Lorenz, K., Schmitt, J. P., Schmitteckert, E. M. & Lohse, M. J. A new type of ERK1/2 autophosphorylation causes cardiac hypertrophy. Nature Medicine 15 (1), 75–83 (2009) . (26) Grüneboom, A. et al. A network of trans-cortical capillaries as mainstay for blood circulation in long bones. Nature Metabolism 1 (2), 236–250 (2019) . (27) Ounkomol, C., Seshamani, S., Maleckar, M. M., Collman, F. & Johnson, G. R. Label-free prediction of three-dimensional fluorescence images from transmitted-light microscopy. Nature Methods 15 (11), 917–920 (2018) . (28) Viana, M. P. et al. Integrated intracellular organization and its variations in human iPS cells. Nature 613 (7943), 345–354 (2023) . Wollman, R. & Stuurman, N. High throughput microscopy: from raw images to discoveries. Journal of Cell Science 120 (21), 3715–3722 (2007) . (4) Gómez-de Mariscal, E. et al. DeepImageJ: A user-friendly environment to run deep learning models in ImageJ. Nature Methods 18 (10), 1192–1195 (2021) . (5) von Chamier, L. et al. Democratising deep learning for microscopy with ZeroCostDL4Mic. Nature Communications 12 (1), 2276 (2021) . (6) Pachitariu, M. & Stringer, C. Cellpose 2.0: how to train your own model. Nature Methods 19 (12), 1634–1641 (2022) . (7) Sonneck, J. & Chen, J. MMV_im2im: An Open Source Microscopy Machine Vision Toolbox for Image-to-Image Transformation (2023). ArXiv:2209.02498 [cs]. (8) Mahecic, D. et al. Event-driven acquisition for content-enriched microscopy. Nature Methods 19 (10), 1262–1267 (2022) . (9) Desislavov, R., Martínez-Plumed, F. & Hernández-Orallo, J. Trends in AI inference energy consumption: Beyond the performance-vs-parameter laws of deep learning. Sustainable Computing: Informatics and Systems 38, 100857 (2023) . (10) Xu, X. et al. Quantization of Fully Convolutional Networks for Accurate Biomedical Image Segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2018) . (11) Gou, J., Yu, B., Maybank, S. J. & Tao, D. Knowledge Distillation: A Survey. International Journal of Computer Vision 129 (6), 1789–1819 (2021) . (12) Ding, C. et al. REQ-YOLO: A Resource-Aware, Efficient Quantization Framework for Object Detection on FPGAs (2019). ArXiv:1909.13396 [cs]. (13) Kozlov, A., Lazarevich, I., Shamporov, V., Lyalyushkin, N. & Gorbachev, Y. Neural Network Compression Framework for fast model inference (2020). ArXiv:2002.08679 [cs, eess]. (14) Li, Y. et al. MQBench: Towards Reproducible and Deployable Model Quantization Benchmark. Neural Information Processing Systems (2021) . (15) Siddegowda, S. et al. Neural Network Quantization with AI Model Efficiency Toolkit (AIMET) (2022). ArXiv:2201.08442 [cs.LG]. (16) Microsoft. Neural Network Intelligence (2022). URL https://github.com/microsoft/nni. (17) AskariHemmat, M. et al. U-Net Fixed-Point Quantization for Medical Image Segmentation. Large-Scale Annotation of Biomedical Data and Expert Label Synthesis and Hardware Aware Learning for Medical Imaging and Computer Assisted Intervention 115–124 (2019) . (18) Micikevicius, P. et al. FP8 Formats for Deep Learning (2022). ArXiv:2209.05433 [cs]. (19) Han, S., Pool, J., Tran, J. & Dally, W. J. Learning both Weights and Connections for Efficient Neural Networks (2015). ArXiv:1506.02626 [cs] . (20) He, Y., Liu, P., Wang, Z., Hu, Z. & Yang, Y. Filter Pruning via Geometric Median for Deep Convolutional Neural Networks Acceleration (2018). ArXiv:1811.00250 [cs] . (21) Weigert, M. et al. Content-aware image restoration: pushing the limits of fluorescence microscopy. Nature Methods 15 (12), 1090–1097 (2018) . (22) Cardoso, M. J. et al. MONAI: An open-source framework for deep learning in healthcare (2022). ArXiv:2211.02701 [cs] . (23) Cardiff, R. D., Miller, C. H. & Munn, R. J. Manual Hematoxylin and Eosin Staining of Mouse Tissue Sections. Cold Spring Harbor Protocols 2014 (6), pdb.prot073411 (2014) . (24) Tomasovic, A. et al. Interference with ERK-dimerization at the nucleocytosolic interface targets pathological ERK1/2 signaling without cardiotoxic side-effects. Nature Communications 11 (1), 1733 (2020) . (25) Lorenz, K., Schmitt, J. P., Schmitteckert, E. M. & Lohse, M. J. A new type of ERK1/2 autophosphorylation causes cardiac hypertrophy. Nature Medicine 15 (1), 75–83 (2009) . (26) Grüneboom, A. et al. A network of trans-cortical capillaries as mainstay for blood circulation in long bones. Nature Metabolism 1 (2), 236–250 (2019) . (27) Ounkomol, C., Seshamani, S., Maleckar, M. M., Collman, F. & Johnson, G. R. Label-free prediction of three-dimensional fluorescence images from transmitted-light microscopy. Nature Methods 15 (11), 917–920 (2018) . (28) Viana, M. P. et al. Integrated intracellular organization and its variations in human iPS cells. Nature 613 (7943), 345–354 (2023) . Gómez-de Mariscal, E. et al. DeepImageJ: A user-friendly environment to run deep learning models in ImageJ. Nature Methods 18 (10), 1192–1195 (2021) . (5) von Chamier, L. et al. Democratising deep learning for microscopy with ZeroCostDL4Mic. Nature Communications 12 (1), 2276 (2021) . (6) Pachitariu, M. & Stringer, C. Cellpose 2.0: how to train your own model. Nature Methods 19 (12), 1634–1641 (2022) . (7) Sonneck, J. & Chen, J. MMV_im2im: An Open Source Microscopy Machine Vision Toolbox for Image-to-Image Transformation (2023). ArXiv:2209.02498 [cs]. (8) Mahecic, D. et al. Event-driven acquisition for content-enriched microscopy. Nature Methods 19 (10), 1262–1267 (2022) . (9) Desislavov, R., Martínez-Plumed, F. & Hernández-Orallo, J. Trends in AI inference energy consumption: Beyond the performance-vs-parameter laws of deep learning. Sustainable Computing: Informatics and Systems 38, 100857 (2023) . (10) Xu, X. et al. Quantization of Fully Convolutional Networks for Accurate Biomedical Image Segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2018) . (11) Gou, J., Yu, B., Maybank, S. J. & Tao, D. Knowledge Distillation: A Survey. International Journal of Computer Vision 129 (6), 1789–1819 (2021) . (12) Ding, C. et al. REQ-YOLO: A Resource-Aware, Efficient Quantization Framework for Object Detection on FPGAs (2019). ArXiv:1909.13396 [cs]. (13) Kozlov, A., Lazarevich, I., Shamporov, V., Lyalyushkin, N. & Gorbachev, Y. Neural Network Compression Framework for fast model inference (2020). ArXiv:2002.08679 [cs, eess]. (14) Li, Y. et al. MQBench: Towards Reproducible and Deployable Model Quantization Benchmark. Neural Information Processing Systems (2021) . (15) Siddegowda, S. et al. Neural Network Quantization with AI Model Efficiency Toolkit (AIMET) (2022). ArXiv:2201.08442 [cs.LG]. (16) Microsoft. Neural Network Intelligence (2022). URL https://github.com/microsoft/nni. (17) AskariHemmat, M. et al. U-Net Fixed-Point Quantization for Medical Image Segmentation. Large-Scale Annotation of Biomedical Data and Expert Label Synthesis and Hardware Aware Learning for Medical Imaging and Computer Assisted Intervention 115–124 (2019) . (18) Micikevicius, P. et al. FP8 Formats for Deep Learning (2022). ArXiv:2209.05433 [cs]. (19) Han, S., Pool, J., Tran, J. & Dally, W. J. Learning both Weights and Connections for Efficient Neural Networks (2015). ArXiv:1506.02626 [cs] . (20) He, Y., Liu, P., Wang, Z., Hu, Z. & Yang, Y. Filter Pruning via Geometric Median for Deep Convolutional Neural Networks Acceleration (2018). ArXiv:1811.00250 [cs] . (21) Weigert, M. et al. Content-aware image restoration: pushing the limits of fluorescence microscopy. Nature Methods 15 (12), 1090–1097 (2018) . (22) Cardoso, M. J. et al. MONAI: An open-source framework for deep learning in healthcare (2022). ArXiv:2211.02701 [cs] . (23) Cardiff, R. D., Miller, C. H. & Munn, R. J. Manual Hematoxylin and Eosin Staining of Mouse Tissue Sections. Cold Spring Harbor Protocols 2014 (6), pdb.prot073411 (2014) . (24) Tomasovic, A. et al. Interference with ERK-dimerization at the nucleocytosolic interface targets pathological ERK1/2 signaling without cardiotoxic side-effects. Nature Communications 11 (1), 1733 (2020) . (25) Lorenz, K., Schmitt, J. P., Schmitteckert, E. M. & Lohse, M. J. A new type of ERK1/2 autophosphorylation causes cardiac hypertrophy. Nature Medicine 15 (1), 75–83 (2009) . (26) Grüneboom, A. et al. A network of trans-cortical capillaries as mainstay for blood circulation in long bones. Nature Metabolism 1 (2), 236–250 (2019) . (27) Ounkomol, C., Seshamani, S., Maleckar, M. M., Collman, F. & Johnson, G. R. Label-free prediction of three-dimensional fluorescence images from transmitted-light microscopy. Nature Methods 15 (11), 917–920 (2018) . (28) Viana, M. P. et al. Integrated intracellular organization and its variations in human iPS cells. Nature 613 (7943), 345–354 (2023) . von Chamier, L. et al. Democratising deep learning for microscopy with ZeroCostDL4Mic. Nature Communications 12 (1), 2276 (2021) . (6) Pachitariu, M. & Stringer, C. Cellpose 2.0: how to train your own model. Nature Methods 19 (12), 1634–1641 (2022) . (7) Sonneck, J. & Chen, J. MMV_im2im: An Open Source Microscopy Machine Vision Toolbox for Image-to-Image Transformation (2023). ArXiv:2209.02498 [cs]. (8) Mahecic, D. et al. Event-driven acquisition for content-enriched microscopy. Nature Methods 19 (10), 1262–1267 (2022) . (9) Desislavov, R., Martínez-Plumed, F. & Hernández-Orallo, J. Trends in AI inference energy consumption: Beyond the performance-vs-parameter laws of deep learning. Sustainable Computing: Informatics and Systems 38, 100857 (2023) . (10) Xu, X. et al. Quantization of Fully Convolutional Networks for Accurate Biomedical Image Segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2018) . (11) Gou, J., Yu, B., Maybank, S. J. & Tao, D. Knowledge Distillation: A Survey. International Journal of Computer Vision 129 (6), 1789–1819 (2021) . (12) Ding, C. et al. REQ-YOLO: A Resource-Aware, Efficient Quantization Framework for Object Detection on FPGAs (2019). ArXiv:1909.13396 [cs]. (13) Kozlov, A., Lazarevich, I., Shamporov, V., Lyalyushkin, N. & Gorbachev, Y. Neural Network Compression Framework for fast model inference (2020). ArXiv:2002.08679 [cs, eess]. (14) Li, Y. et al. MQBench: Towards Reproducible and Deployable Model Quantization Benchmark. Neural Information Processing Systems (2021) . (15) Siddegowda, S. et al. Neural Network Quantization with AI Model Efficiency Toolkit (AIMET) (2022). ArXiv:2201.08442 [cs.LG]. (16) Microsoft. Neural Network Intelligence (2022). URL https://github.com/microsoft/nni. (17) AskariHemmat, M. et al. U-Net Fixed-Point Quantization for Medical Image Segmentation. Large-Scale Annotation of Biomedical Data and Expert Label Synthesis and Hardware Aware Learning for Medical Imaging and Computer Assisted Intervention 115–124 (2019) . (18) Micikevicius, P. et al. FP8 Formats for Deep Learning (2022). ArXiv:2209.05433 [cs]. (19) Han, S., Pool, J., Tran, J. & Dally, W. J. Learning both Weights and Connections for Efficient Neural Networks (2015). ArXiv:1506.02626 [cs] . (20) He, Y., Liu, P., Wang, Z., Hu, Z. & Yang, Y. Filter Pruning via Geometric Median for Deep Convolutional Neural Networks Acceleration (2018). ArXiv:1811.00250 [cs] . (21) Weigert, M. et al. Content-aware image restoration: pushing the limits of fluorescence microscopy. Nature Methods 15 (12), 1090–1097 (2018) . (22) Cardoso, M. J. et al. MONAI: An open-source framework for deep learning in healthcare (2022). ArXiv:2211.02701 [cs] . (23) Cardiff, R. D., Miller, C. H. & Munn, R. J. Manual Hematoxylin and Eosin Staining of Mouse Tissue Sections. Cold Spring Harbor Protocols 2014 (6), pdb.prot073411 (2014) . (24) Tomasovic, A. et al. Interference with ERK-dimerization at the nucleocytosolic interface targets pathological ERK1/2 signaling without cardiotoxic side-effects. Nature Communications 11 (1), 1733 (2020) . (25) Lorenz, K., Schmitt, J. P., Schmitteckert, E. M. & Lohse, M. J. A new type of ERK1/2 autophosphorylation causes cardiac hypertrophy. Nature Medicine 15 (1), 75–83 (2009) . (26) Grüneboom, A. et al. A network of trans-cortical capillaries as mainstay for blood circulation in long bones. Nature Metabolism 1 (2), 236–250 (2019) . (27) Ounkomol, C., Seshamani, S., Maleckar, M. M., Collman, F. & Johnson, G. R. Label-free prediction of three-dimensional fluorescence images from transmitted-light microscopy. Nature Methods 15 (11), 917–920 (2018) . (28) Viana, M. P. et al. Integrated intracellular organization and its variations in human iPS cells. Nature 613 (7943), 345–354 (2023) . Pachitariu, M. & Stringer, C. Cellpose 2.0: how to train your own model. Nature Methods 19 (12), 1634–1641 (2022) . (7) Sonneck, J. & Chen, J. MMV_im2im: An Open Source Microscopy Machine Vision Toolbox for Image-to-Image Transformation (2023). ArXiv:2209.02498 [cs]. (8) Mahecic, D. et al. Event-driven acquisition for content-enriched microscopy. Nature Methods 19 (10), 1262–1267 (2022) . (9) Desislavov, R., Martínez-Plumed, F. & Hernández-Orallo, J. Trends in AI inference energy consumption: Beyond the performance-vs-parameter laws of deep learning. Sustainable Computing: Informatics and Systems 38, 100857 (2023) . (10) Xu, X. et al. Quantization of Fully Convolutional Networks for Accurate Biomedical Image Segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2018) . (11) Gou, J., Yu, B., Maybank, S. J. & Tao, D. Knowledge Distillation: A Survey. International Journal of Computer Vision 129 (6), 1789–1819 (2021) . (12) Ding, C. et al. REQ-YOLO: A Resource-Aware, Efficient Quantization Framework for Object Detection on FPGAs (2019). ArXiv:1909.13396 [cs]. (13) Kozlov, A., Lazarevich, I., Shamporov, V., Lyalyushkin, N. & Gorbachev, Y. Neural Network Compression Framework for fast model inference (2020). ArXiv:2002.08679 [cs, eess]. (14) Li, Y. et al. MQBench: Towards Reproducible and Deployable Model Quantization Benchmark. Neural Information Processing Systems (2021) . (15) Siddegowda, S. et al. Neural Network Quantization with AI Model Efficiency Toolkit (AIMET) (2022). ArXiv:2201.08442 [cs.LG]. (16) Microsoft. Neural Network Intelligence (2022). URL https://github.com/microsoft/nni. (17) AskariHemmat, M. et al. U-Net Fixed-Point Quantization for Medical Image Segmentation. Large-Scale Annotation of Biomedical Data and Expert Label Synthesis and Hardware Aware Learning for Medical Imaging and Computer Assisted Intervention 115–124 (2019) . (18) Micikevicius, P. et al. FP8 Formats for Deep Learning (2022). ArXiv:2209.05433 [cs]. (19) Han, S., Pool, J., Tran, J. & Dally, W. J. Learning both Weights and Connections for Efficient Neural Networks (2015). ArXiv:1506.02626 [cs] . (20) He, Y., Liu, P., Wang, Z., Hu, Z. & Yang, Y. Filter Pruning via Geometric Median for Deep Convolutional Neural Networks Acceleration (2018). ArXiv:1811.00250 [cs] . (21) Weigert, M. et al. Content-aware image restoration: pushing the limits of fluorescence microscopy. Nature Methods 15 (12), 1090–1097 (2018) . (22) Cardoso, M. J. et al. MONAI: An open-source framework for deep learning in healthcare (2022). ArXiv:2211.02701 [cs] . 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REQ-YOLO: A Resource-Aware, Efficient Quantization Framework for Object Detection on FPGAs (2019). ArXiv:1909.13396 [cs]. (13) Kozlov, A., Lazarevich, I., Shamporov, V., Lyalyushkin, N. & Gorbachev, Y. Neural Network Compression Framework for fast model inference (2020). ArXiv:2002.08679 [cs, eess]. (14) Li, Y. et al. MQBench: Towards Reproducible and Deployable Model Quantization Benchmark. Neural Information Processing Systems (2021) . (15) Siddegowda, S. et al. Neural Network Quantization with AI Model Efficiency Toolkit (AIMET) (2022). ArXiv:2201.08442 [cs.LG]. (16) Microsoft. Neural Network Intelligence (2022). URL https://github.com/microsoft/nni. (17) AskariHemmat, M. et al. U-Net Fixed-Point Quantization for Medical Image Segmentation. Large-Scale Annotation of Biomedical Data and Expert Label Synthesis and Hardware Aware Learning for Medical Imaging and Computer Assisted Intervention 115–124 (2019) . (18) Micikevicius, P. et al. FP8 Formats for Deep Learning (2022). ArXiv:2209.05433 [cs]. (19) Han, S., Pool, J., Tran, J. & Dally, W. J. Learning both Weights and Connections for Efficient Neural Networks (2015). ArXiv:1506.02626 [cs] . (20) He, Y., Liu, P., Wang, Z., Hu, Z. & Yang, Y. Filter Pruning via Geometric Median for Deep Convolutional Neural Networks Acceleration (2018). ArXiv:1811.00250 [cs] . (21) Weigert, M. et al. Content-aware image restoration: pushing the limits of fluorescence microscopy. Nature Methods 15 (12), 1090–1097 (2018) . (22) Cardoso, M. J. et al. MONAI: An open-source framework for deep learning in healthcare (2022). ArXiv:2211.02701 [cs] . (23) Cardiff, R. D., Miller, C. H. & Munn, R. J. Manual Hematoxylin and Eosin Staining of Mouse Tissue Sections. Cold Spring Harbor Protocols 2014 (6), pdb.prot073411 (2014) . (24) Tomasovic, A. et al. Interference with ERK-dimerization at the nucleocytosolic interface targets pathological ERK1/2 signaling without cardiotoxic side-effects. Nature Communications 11 (1), 1733 (2020) . (25) Lorenz, K., Schmitt, J. P., Schmitteckert, E. M. & Lohse, M. J. A new type of ERK1/2 autophosphorylation causes cardiac hypertrophy. Nature Medicine 15 (1), 75–83 (2009) . (26) Grüneboom, A. et al. A network of trans-cortical capillaries as mainstay for blood circulation in long bones. Nature Metabolism 1 (2), 236–250 (2019) . (27) Ounkomol, C., Seshamani, S., Maleckar, M. M., Collman, F. & Johnson, G. R. Label-free prediction of three-dimensional fluorescence images from transmitted-light microscopy. Nature Methods 15 (11), 917–920 (2018) . (28) Viana, M. P. et al. Integrated intracellular organization and its variations in human iPS cells. Nature 613 (7943), 345–354 (2023) . Mahecic, D. et al. Event-driven acquisition for content-enriched microscopy. Nature Methods 19 (10), 1262–1267 (2022) . (9) Desislavov, R., Martínez-Plumed, F. & Hernández-Orallo, J. Trends in AI inference energy consumption: Beyond the performance-vs-parameter laws of deep learning. Sustainable Computing: Informatics and Systems 38, 100857 (2023) . (10) Xu, X. et al. Quantization of Fully Convolutional Networks for Accurate Biomedical Image Segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2018) . (11) Gou, J., Yu, B., Maybank, S. J. & Tao, D. Knowledge Distillation: A Survey. International Journal of Computer Vision 129 (6), 1789–1819 (2021) . (12) Ding, C. et al. REQ-YOLO: A Resource-Aware, Efficient Quantization Framework for Object Detection on FPGAs (2019). ArXiv:1909.13396 [cs]. (13) Kozlov, A., Lazarevich, I., Shamporov, V., Lyalyushkin, N. & Gorbachev, Y. Neural Network Compression Framework for fast model inference (2020). ArXiv:2002.08679 [cs, eess]. (14) Li, Y. et al. MQBench: Towards Reproducible and Deployable Model Quantization Benchmark. Neural Information Processing Systems (2021) . (15) Siddegowda, S. et al. Neural Network Quantization with AI Model Efficiency Toolkit (AIMET) (2022). ArXiv:2201.08442 [cs.LG]. (16) Microsoft. Neural Network Intelligence (2022). URL https://github.com/microsoft/nni. (17) AskariHemmat, M. et al. U-Net Fixed-Point Quantization for Medical Image Segmentation. Large-Scale Annotation of Biomedical Data and Expert Label Synthesis and Hardware Aware Learning for Medical Imaging and Computer Assisted Intervention 115–124 (2019) . (18) Micikevicius, P. et al. FP8 Formats for Deep Learning (2022). ArXiv:2209.05433 [cs]. (19) Han, S., Pool, J., Tran, J. & Dally, W. J. Learning both Weights and Connections for Efficient Neural Networks (2015). ArXiv:1506.02626 [cs] . (20) He, Y., Liu, P., Wang, Z., Hu, Z. & Yang, Y. Filter Pruning via Geometric Median for Deep Convolutional Neural Networks Acceleration (2018). ArXiv:1811.00250 [cs] . (21) Weigert, M. et al. Content-aware image restoration: pushing the limits of fluorescence microscopy. Nature Methods 15 (12), 1090–1097 (2018) . (22) Cardoso, M. J. et al. MONAI: An open-source framework for deep learning in healthcare (2022). ArXiv:2211.02701 [cs] . (23) Cardiff, R. D., Miller, C. H. & Munn, R. J. Manual Hematoxylin and Eosin Staining of Mouse Tissue Sections. Cold Spring Harbor Protocols 2014 (6), pdb.prot073411 (2014) . (24) Tomasovic, A. et al. Interference with ERK-dimerization at the nucleocytosolic interface targets pathological ERK1/2 signaling without cardiotoxic side-effects. Nature Communications 11 (1), 1733 (2020) . (25) Lorenz, K., Schmitt, J. P., Schmitteckert, E. M. & Lohse, M. J. A new type of ERK1/2 autophosphorylation causes cardiac hypertrophy. Nature Medicine 15 (1), 75–83 (2009) . (26) Grüneboom, A. et al. A network of trans-cortical capillaries as mainstay for blood circulation in long bones. Nature Metabolism 1 (2), 236–250 (2019) . (27) Ounkomol, C., Seshamani, S., Maleckar, M. M., Collman, F. & Johnson, G. R. Label-free prediction of three-dimensional fluorescence images from transmitted-light microscopy. Nature Methods 15 (11), 917–920 (2018) . (28) Viana, M. P. et al. Integrated intracellular organization and its variations in human iPS cells. Nature 613 (7943), 345–354 (2023) . Desislavov, R., Martínez-Plumed, F. & Hernández-Orallo, J. Trends in AI inference energy consumption: Beyond the performance-vs-parameter laws of deep learning. Sustainable Computing: Informatics and Systems 38, 100857 (2023) . (10) Xu, X. et al. Quantization of Fully Convolutional Networks for Accurate Biomedical Image Segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2018) . (11) Gou, J., Yu, B., Maybank, S. J. & Tao, D. Knowledge Distillation: A Survey. International Journal of Computer Vision 129 (6), 1789–1819 (2021) . (12) Ding, C. et al. REQ-YOLO: A Resource-Aware, Efficient Quantization Framework for Object Detection on FPGAs (2019). ArXiv:1909.13396 [cs]. (13) Kozlov, A., Lazarevich, I., Shamporov, V., Lyalyushkin, N. & Gorbachev, Y. Neural Network Compression Framework for fast model inference (2020). ArXiv:2002.08679 [cs, eess]. (14) Li, Y. et al. MQBench: Towards Reproducible and Deployable Model Quantization Benchmark. Neural Information Processing Systems (2021) . (15) Siddegowda, S. et al. Neural Network Quantization with AI Model Efficiency Toolkit (AIMET) (2022). ArXiv:2201.08442 [cs.LG]. (16) Microsoft. Neural Network Intelligence (2022). URL https://github.com/microsoft/nni. (17) AskariHemmat, M. et al. U-Net Fixed-Point Quantization for Medical Image Segmentation. Large-Scale Annotation of Biomedical Data and Expert Label Synthesis and Hardware Aware Learning for Medical Imaging and Computer Assisted Intervention 115–124 (2019) . (18) Micikevicius, P. et al. FP8 Formats for Deep Learning (2022). ArXiv:2209.05433 [cs]. (19) Han, S., Pool, J., Tran, J. & Dally, W. J. Learning both Weights and Connections for Efficient Neural Networks (2015). ArXiv:1506.02626 [cs] . (20) He, Y., Liu, P., Wang, Z., Hu, Z. & Yang, Y. Filter Pruning via Geometric Median for Deep Convolutional Neural Networks Acceleration (2018). ArXiv:1811.00250 [cs] . (21) Weigert, M. et al. Content-aware image restoration: pushing the limits of fluorescence microscopy. Nature Methods 15 (12), 1090–1097 (2018) . (22) Cardoso, M. J. et al. MONAI: An open-source framework for deep learning in healthcare (2022). ArXiv:2211.02701 [cs] . (23) Cardiff, R. D., Miller, C. H. & Munn, R. J. Manual Hematoxylin and Eosin Staining of Mouse Tissue Sections. Cold Spring Harbor Protocols 2014 (6), pdb.prot073411 (2014) . (24) Tomasovic, A. et al. Interference with ERK-dimerization at the nucleocytosolic interface targets pathological ERK1/2 signaling without cardiotoxic side-effects. Nature Communications 11 (1), 1733 (2020) . (25) Lorenz, K., Schmitt, J. P., Schmitteckert, E. M. & Lohse, M. J. A new type of ERK1/2 autophosphorylation causes cardiac hypertrophy. Nature Medicine 15 (1), 75–83 (2009) . (26) Grüneboom, A. et al. A network of trans-cortical capillaries as mainstay for blood circulation in long bones. Nature Metabolism 1 (2), 236–250 (2019) . (27) Ounkomol, C., Seshamani, S., Maleckar, M. M., Collman, F. & Johnson, G. R. Label-free prediction of three-dimensional fluorescence images from transmitted-light microscopy. Nature Methods 15 (11), 917–920 (2018) . (28) Viana, M. P. et al. Integrated intracellular organization and its variations in human iPS cells. Nature 613 (7943), 345–354 (2023) . Xu, X. et al. Quantization of Fully Convolutional Networks for Accurate Biomedical Image Segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2018) . (11) Gou, J., Yu, B., Maybank, S. J. & Tao, D. Knowledge Distillation: A Survey. International Journal of Computer Vision 129 (6), 1789–1819 (2021) . (12) Ding, C. et al. REQ-YOLO: A Resource-Aware, Efficient Quantization Framework for Object Detection on FPGAs (2019). ArXiv:1909.13396 [cs]. (13) Kozlov, A., Lazarevich, I., Shamporov, V., Lyalyushkin, N. & Gorbachev, Y. Neural Network Compression Framework for fast model inference (2020). ArXiv:2002.08679 [cs, eess]. (14) Li, Y. et al. MQBench: Towards Reproducible and Deployable Model Quantization Benchmark. Neural Information Processing Systems (2021) . (15) Siddegowda, S. et al. Neural Network Quantization with AI Model Efficiency Toolkit (AIMET) (2022). ArXiv:2201.08442 [cs.LG]. (16) Microsoft. Neural Network Intelligence (2022). URL https://github.com/microsoft/nni. (17) AskariHemmat, M. et al. U-Net Fixed-Point Quantization for Medical Image Segmentation. Large-Scale Annotation of Biomedical Data and Expert Label Synthesis and Hardware Aware Learning for Medical Imaging and Computer Assisted Intervention 115–124 (2019) . (18) Micikevicius, P. et al. FP8 Formats for Deep Learning (2022). ArXiv:2209.05433 [cs]. (19) Han, S., Pool, J., Tran, J. & Dally, W. J. Learning both Weights and Connections for Efficient Neural Networks (2015). ArXiv:1506.02626 [cs] . (20) He, Y., Liu, P., Wang, Z., Hu, Z. & Yang, Y. Filter Pruning via Geometric Median for Deep Convolutional Neural Networks Acceleration (2018). ArXiv:1811.00250 [cs] . (21) Weigert, M. et al. Content-aware image restoration: pushing the limits of fluorescence microscopy. Nature Methods 15 (12), 1090–1097 (2018) . (22) Cardoso, M. J. et al. MONAI: An open-source framework for deep learning in healthcare (2022). ArXiv:2211.02701 [cs] . (23) Cardiff, R. D., Miller, C. H. & Munn, R. J. Manual Hematoxylin and Eosin Staining of Mouse Tissue Sections. Cold Spring Harbor Protocols 2014 (6), pdb.prot073411 (2014) . (24) Tomasovic, A. et al. Interference with ERK-dimerization at the nucleocytosolic interface targets pathological ERK1/2 signaling without cardiotoxic side-effects. Nature Communications 11 (1), 1733 (2020) . (25) Lorenz, K., Schmitt, J. P., Schmitteckert, E. M. & Lohse, M. J. A new type of ERK1/2 autophosphorylation causes cardiac hypertrophy. Nature Medicine 15 (1), 75–83 (2009) . (26) Grüneboom, A. et al. A network of trans-cortical capillaries as mainstay for blood circulation in long bones. Nature Metabolism 1 (2), 236–250 (2019) . (27) Ounkomol, C., Seshamani, S., Maleckar, M. M., Collman, F. & Johnson, G. R. Label-free prediction of three-dimensional fluorescence images from transmitted-light microscopy. Nature Methods 15 (11), 917–920 (2018) . (28) Viana, M. P. et al. Integrated intracellular organization and its variations in human iPS cells. Nature 613 (7943), 345–354 (2023) . Gou, J., Yu, B., Maybank, S. J. & Tao, D. Knowledge Distillation: A Survey. International Journal of Computer Vision 129 (6), 1789–1819 (2021) . (12) Ding, C. et al. REQ-YOLO: A Resource-Aware, Efficient Quantization Framework for Object Detection on FPGAs (2019). ArXiv:1909.13396 [cs]. (13) Kozlov, A., Lazarevich, I., Shamporov, V., Lyalyushkin, N. & Gorbachev, Y. Neural Network Compression Framework for fast model inference (2020). ArXiv:2002.08679 [cs, eess]. (14) Li, Y. et al. MQBench: Towards Reproducible and Deployable Model Quantization Benchmark. Neural Information Processing Systems (2021) . (15) Siddegowda, S. et al. Neural Network Quantization with AI Model Efficiency Toolkit (AIMET) (2022). ArXiv:2201.08442 [cs.LG]. (16) Microsoft. Neural Network Intelligence (2022). URL https://github.com/microsoft/nni. (17) AskariHemmat, M. et al. U-Net Fixed-Point Quantization for Medical Image Segmentation. Large-Scale Annotation of Biomedical Data and Expert Label Synthesis and Hardware Aware Learning for Medical Imaging and Computer Assisted Intervention 115–124 (2019) . (18) Micikevicius, P. et al. FP8 Formats for Deep Learning (2022). ArXiv:2209.05433 [cs]. (19) Han, S., Pool, J., Tran, J. & Dally, W. J. Learning both Weights and Connections for Efficient Neural Networks (2015). ArXiv:1506.02626 [cs] . (20) He, Y., Liu, P., Wang, Z., Hu, Z. & Yang, Y. Filter Pruning via Geometric Median for Deep Convolutional Neural Networks Acceleration (2018). ArXiv:1811.00250 [cs] . (21) Weigert, M. et al. Content-aware image restoration: pushing the limits of fluorescence microscopy. Nature Methods 15 (12), 1090–1097 (2018) . (22) Cardoso, M. J. et al. MONAI: An open-source framework for deep learning in healthcare (2022). ArXiv:2211.02701 [cs] . (23) Cardiff, R. D., Miller, C. H. & Munn, R. J. Manual Hematoxylin and Eosin Staining of Mouse Tissue Sections. Cold Spring Harbor Protocols 2014 (6), pdb.prot073411 (2014) . (24) Tomasovic, A. et al. Interference with ERK-dimerization at the nucleocytosolic interface targets pathological ERK1/2 signaling without cardiotoxic side-effects. Nature Communications 11 (1), 1733 (2020) . (25) Lorenz, K., Schmitt, J. P., Schmitteckert, E. M. & Lohse, M. J. A new type of ERK1/2 autophosphorylation causes cardiac hypertrophy. Nature Medicine 15 (1), 75–83 (2009) . (26) Grüneboom, A. et al. A network of trans-cortical capillaries as mainstay for blood circulation in long bones. Nature Metabolism 1 (2), 236–250 (2019) . (27) Ounkomol, C., Seshamani, S., Maleckar, M. M., Collman, F. & Johnson, G. R. Label-free prediction of three-dimensional fluorescence images from transmitted-light microscopy. Nature Methods 15 (11), 917–920 (2018) . (28) Viana, M. P. et al. Integrated intracellular organization and its variations in human iPS cells. Nature 613 (7943), 345–354 (2023) . Ding, C. et al. REQ-YOLO: A Resource-Aware, Efficient Quantization Framework for Object Detection on FPGAs (2019). ArXiv:1909.13396 [cs]. (13) Kozlov, A., Lazarevich, I., Shamporov, V., Lyalyushkin, N. & Gorbachev, Y. Neural Network Compression Framework for fast model inference (2020). ArXiv:2002.08679 [cs, eess]. (14) Li, Y. et al. MQBench: Towards Reproducible and Deployable Model Quantization Benchmark. Neural Information Processing Systems (2021) . (15) Siddegowda, S. et al. Neural Network Quantization with AI Model Efficiency Toolkit (AIMET) (2022). ArXiv:2201.08442 [cs.LG]. (16) Microsoft. Neural Network Intelligence (2022). URL https://github.com/microsoft/nni. (17) AskariHemmat, M. et al. U-Net Fixed-Point Quantization for Medical Image Segmentation. Large-Scale Annotation of Biomedical Data and Expert Label Synthesis and Hardware Aware Learning for Medical Imaging and Computer Assisted Intervention 115–124 (2019) . (18) Micikevicius, P. et al. FP8 Formats for Deep Learning (2022). ArXiv:2209.05433 [cs]. (19) Han, S., Pool, J., Tran, J. & Dally, W. J. Learning both Weights and Connections for Efficient Neural Networks (2015). ArXiv:1506.02626 [cs] . (20) He, Y., Liu, P., Wang, Z., Hu, Z. & Yang, Y. Filter Pruning via Geometric Median for Deep Convolutional Neural Networks Acceleration (2018). ArXiv:1811.00250 [cs] . (21) Weigert, M. et al. Content-aware image restoration: pushing the limits of fluorescence microscopy. Nature Methods 15 (12), 1090–1097 (2018) . (22) Cardoso, M. J. et al. MONAI: An open-source framework for deep learning in healthcare (2022). ArXiv:2211.02701 [cs] . (23) Cardiff, R. D., Miller, C. H. & Munn, R. J. Manual Hematoxylin and Eosin Staining of Mouse Tissue Sections. Cold Spring Harbor Protocols 2014 (6), pdb.prot073411 (2014) . (24) Tomasovic, A. et al. Interference with ERK-dimerization at the nucleocytosolic interface targets pathological ERK1/2 signaling without cardiotoxic side-effects. Nature Communications 11 (1), 1733 (2020) . (25) Lorenz, K., Schmitt, J. P., Schmitteckert, E. M. & Lohse, M. J. A new type of ERK1/2 autophosphorylation causes cardiac hypertrophy. Nature Medicine 15 (1), 75–83 (2009) . (26) Grüneboom, A. et al. A network of trans-cortical capillaries as mainstay for blood circulation in long bones. Nature Metabolism 1 (2), 236–250 (2019) . (27) Ounkomol, C., Seshamani, S., Maleckar, M. M., Collman, F. & Johnson, G. R. Label-free prediction of three-dimensional fluorescence images from transmitted-light microscopy. Nature Methods 15 (11), 917–920 (2018) . (28) Viana, M. P. et al. Integrated intracellular organization and its variations in human iPS cells. Nature 613 (7943), 345–354 (2023) . Kozlov, A., Lazarevich, I., Shamporov, V., Lyalyushkin, N. & Gorbachev, Y. Neural Network Compression Framework for fast model inference (2020). ArXiv:2002.08679 [cs, eess]. (14) Li, Y. et al. MQBench: Towards Reproducible and Deployable Model Quantization Benchmark. Neural Information Processing Systems (2021) . (15) Siddegowda, S. et al. Neural Network Quantization with AI Model Efficiency Toolkit (AIMET) (2022). ArXiv:2201.08442 [cs.LG]. (16) Microsoft. Neural Network Intelligence (2022). URL https://github.com/microsoft/nni. (17) AskariHemmat, M. et al. U-Net Fixed-Point Quantization for Medical Image Segmentation. Large-Scale Annotation of Biomedical Data and Expert Label Synthesis and Hardware Aware Learning for Medical Imaging and Computer Assisted Intervention 115–124 (2019) . (18) Micikevicius, P. et al. FP8 Formats for Deep Learning (2022). ArXiv:2209.05433 [cs]. (19) Han, S., Pool, J., Tran, J. & Dally, W. J. Learning both Weights and Connections for Efficient Neural Networks (2015). ArXiv:1506.02626 [cs] . (20) He, Y., Liu, P., Wang, Z., Hu, Z. & Yang, Y. Filter Pruning via Geometric Median for Deep Convolutional Neural Networks Acceleration (2018). ArXiv:1811.00250 [cs] . (21) Weigert, M. et al. Content-aware image restoration: pushing the limits of fluorescence microscopy. Nature Methods 15 (12), 1090–1097 (2018) . (22) Cardoso, M. J. et al. MONAI: An open-source framework for deep learning in healthcare (2022). ArXiv:2211.02701 [cs] . (23) Cardiff, R. D., Miller, C. H. & Munn, R. J. 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(27) Ounkomol, C., Seshamani, S., Maleckar, M. M., Collman, F. & Johnson, G. R. Label-free prediction of three-dimensional fluorescence images from transmitted-light microscopy. Nature Methods 15 (11), 917–920 (2018) . (28) Viana, M. P. et al. Integrated intracellular organization and its variations in human iPS cells. Nature 613 (7943), 345–354 (2023) . Wollman, R. & Stuurman, N. High throughput microscopy: from raw images to discoveries. Journal of Cell Science 120 (21), 3715–3722 (2007) . (4) Gómez-de Mariscal, E. et al. DeepImageJ: A user-friendly environment to run deep learning models in ImageJ. Nature Methods 18 (10), 1192–1195 (2021) . (5) von Chamier, L. et al. Democratising deep learning for microscopy with ZeroCostDL4Mic. Nature Communications 12 (1), 2276 (2021) . (6) Pachitariu, M. & Stringer, C. Cellpose 2.0: how to train your own model. Nature Methods 19 (12), 1634–1641 (2022) . (7) Sonneck, J. & Chen, J. MMV_im2im: An Open Source Microscopy Machine Vision Toolbox for Image-to-Image Transformation (2023). ArXiv:2209.02498 [cs]. (8) Mahecic, D. et al. Event-driven acquisition for content-enriched microscopy. Nature Methods 19 (10), 1262–1267 (2022) . (9) Desislavov, R., Martínez-Plumed, F. & Hernández-Orallo, J. Trends in AI inference energy consumption: Beyond the performance-vs-parameter laws of deep learning. Sustainable Computing: Informatics and Systems 38, 100857 (2023) . (10) Xu, X. et al. Quantization of Fully Convolutional Networks for Accurate Biomedical Image Segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2018) . (11) Gou, J., Yu, B., Maybank, S. J. & Tao, D. Knowledge Distillation: A Survey. International Journal of Computer Vision 129 (6), 1789–1819 (2021) . (12) Ding, C. et al. REQ-YOLO: A Resource-Aware, Efficient Quantization Framework for Object Detection on FPGAs (2019). ArXiv:1909.13396 [cs]. (13) Kozlov, A., Lazarevich, I., Shamporov, V., Lyalyushkin, N. & Gorbachev, Y. Neural Network Compression Framework for fast model inference (2020). ArXiv:2002.08679 [cs, eess]. (14) Li, Y. et al. MQBench: Towards Reproducible and Deployable Model Quantization Benchmark. Neural Information Processing Systems (2021) . (15) Siddegowda, S. et al. Neural Network Quantization with AI Model Efficiency Toolkit (AIMET) (2022). ArXiv:2201.08442 [cs.LG]. (16) Microsoft. Neural Network Intelligence (2022). URL https://github.com/microsoft/nni. (17) AskariHemmat, M. et al. U-Net Fixed-Point Quantization for Medical Image Segmentation. Large-Scale Annotation of Biomedical Data and Expert Label Synthesis and Hardware Aware Learning for Medical Imaging and Computer Assisted Intervention 115–124 (2019) . (18) Micikevicius, P. et al. FP8 Formats for Deep Learning (2022). ArXiv:2209.05433 [cs]. (19) Han, S., Pool, J., Tran, J. & Dally, W. J. Learning both Weights and Connections for Efficient Neural Networks (2015). ArXiv:1506.02626 [cs] . (20) He, Y., Liu, P., Wang, Z., Hu, Z. & Yang, Y. Filter Pruning via Geometric Median for Deep Convolutional Neural Networks Acceleration (2018). ArXiv:1811.00250 [cs] . (21) Weigert, M. et al. Content-aware image restoration: pushing the limits of fluorescence microscopy. Nature Methods 15 (12), 1090–1097 (2018) . (22) Cardoso, M. J. et al. MONAI: An open-source framework for deep learning in healthcare (2022). ArXiv:2211.02701 [cs] . (23) Cardiff, R. D., Miller, C. H. & Munn, R. J. Manual Hematoxylin and Eosin Staining of Mouse Tissue Sections. Cold Spring Harbor Protocols 2014 (6), pdb.prot073411 (2014) . (24) Tomasovic, A. et al. Interference with ERK-dimerization at the nucleocytosolic interface targets pathological ERK1/2 signaling without cardiotoxic side-effects. Nature Communications 11 (1), 1733 (2020) . (25) Lorenz, K., Schmitt, J. P., Schmitteckert, E. M. & Lohse, M. J. A new type of ERK1/2 autophosphorylation causes cardiac hypertrophy. Nature Medicine 15 (1), 75–83 (2009) . (26) Grüneboom, A. et al. A network of trans-cortical capillaries as mainstay for blood circulation in long bones. Nature Metabolism 1 (2), 236–250 (2019) . (27) Ounkomol, C., Seshamani, S., Maleckar, M. M., Collman, F. & Johnson, G. R. Label-free prediction of three-dimensional fluorescence images from transmitted-light microscopy. Nature Methods 15 (11), 917–920 (2018) . (28) Viana, M. P. et al. Integrated intracellular organization and its variations in human iPS cells. Nature 613 (7943), 345–354 (2023) . Gómez-de Mariscal, E. et al. DeepImageJ: A user-friendly environment to run deep learning models in ImageJ. Nature Methods 18 (10), 1192–1195 (2021) . (5) von Chamier, L. et al. Democratising deep learning for microscopy with ZeroCostDL4Mic. Nature Communications 12 (1), 2276 (2021) . (6) Pachitariu, M. & Stringer, C. Cellpose 2.0: how to train your own model. Nature Methods 19 (12), 1634–1641 (2022) . (7) Sonneck, J. & Chen, J. MMV_im2im: An Open Source Microscopy Machine Vision Toolbox for Image-to-Image Transformation (2023). ArXiv:2209.02498 [cs]. (8) Mahecic, D. et al. Event-driven acquisition for content-enriched microscopy. Nature Methods 19 (10), 1262–1267 (2022) . (9) Desislavov, R., Martínez-Plumed, F. & Hernández-Orallo, J. Trends in AI inference energy consumption: Beyond the performance-vs-parameter laws of deep learning. Sustainable Computing: Informatics and Systems 38, 100857 (2023) . (10) Xu, X. et al. Quantization of Fully Convolutional Networks for Accurate Biomedical Image Segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2018) . (11) Gou, J., Yu, B., Maybank, S. J. & Tao, D. Knowledge Distillation: A Survey. International Journal of Computer Vision 129 (6), 1789–1819 (2021) . (12) Ding, C. et al. REQ-YOLO: A Resource-Aware, Efficient Quantization Framework for Object Detection on FPGAs (2019). ArXiv:1909.13396 [cs]. (13) Kozlov, A., Lazarevich, I., Shamporov, V., Lyalyushkin, N. & Gorbachev, Y. Neural Network Compression Framework for fast model inference (2020). ArXiv:2002.08679 [cs, eess]. (14) Li, Y. et al. MQBench: Towards Reproducible and Deployable Model Quantization Benchmark. Neural Information Processing Systems (2021) . (15) Siddegowda, S. et al. Neural Network Quantization with AI Model Efficiency Toolkit (AIMET) (2022). ArXiv:2201.08442 [cs.LG]. (16) Microsoft. Neural Network Intelligence (2022). URL https://github.com/microsoft/nni. (17) AskariHemmat, M. et al. U-Net Fixed-Point Quantization for Medical Image Segmentation. Large-Scale Annotation of Biomedical Data and Expert Label Synthesis and Hardware Aware Learning for Medical Imaging and Computer Assisted Intervention 115–124 (2019) . (18) Micikevicius, P. et al. FP8 Formats for Deep Learning (2022). ArXiv:2209.05433 [cs]. (19) Han, S., Pool, J., Tran, J. & Dally, W. J. Learning both Weights and Connections for Efficient Neural Networks (2015). ArXiv:1506.02626 [cs] . (20) He, Y., Liu, P., Wang, Z., Hu, Z. & Yang, Y. Filter Pruning via Geometric Median for Deep Convolutional Neural Networks Acceleration (2018). ArXiv:1811.00250 [cs] . (21) Weigert, M. et al. Content-aware image restoration: pushing the limits of fluorescence microscopy. Nature Methods 15 (12), 1090–1097 (2018) . (22) Cardoso, M. J. et al. MONAI: An open-source framework for deep learning in healthcare (2022). ArXiv:2211.02701 [cs] . (23) Cardiff, R. D., Miller, C. H. & Munn, R. J. Manual Hematoxylin and Eosin Staining of Mouse Tissue Sections. Cold Spring Harbor Protocols 2014 (6), pdb.prot073411 (2014) . (24) Tomasovic, A. et al. Interference with ERK-dimerization at the nucleocytosolic interface targets pathological ERK1/2 signaling without cardiotoxic side-effects. Nature Communications 11 (1), 1733 (2020) . (25) Lorenz, K., Schmitt, J. P., Schmitteckert, E. M. & Lohse, M. J. A new type of ERK1/2 autophosphorylation causes cardiac hypertrophy. Nature Medicine 15 (1), 75–83 (2009) . (26) Grüneboom, A. et al. A network of trans-cortical capillaries as mainstay for blood circulation in long bones. Nature Metabolism 1 (2), 236–250 (2019) . (27) Ounkomol, C., Seshamani, S., Maleckar, M. M., Collman, F. & Johnson, G. R. Label-free prediction of three-dimensional fluorescence images from transmitted-light microscopy. Nature Methods 15 (11), 917–920 (2018) . (28) Viana, M. P. et al. Integrated intracellular organization and its variations in human iPS cells. Nature 613 (7943), 345–354 (2023) . von Chamier, L. et al. Democratising deep learning for microscopy with ZeroCostDL4Mic. Nature Communications 12 (1), 2276 (2021) . (6) Pachitariu, M. & Stringer, C. Cellpose 2.0: how to train your own model. Nature Methods 19 (12), 1634–1641 (2022) . (7) Sonneck, J. & Chen, J. MMV_im2im: An Open Source Microscopy Machine Vision Toolbox for Image-to-Image Transformation (2023). ArXiv:2209.02498 [cs]. (8) Mahecic, D. et al. Event-driven acquisition for content-enriched microscopy. Nature Methods 19 (10), 1262–1267 (2022) . (9) Desislavov, R., Martínez-Plumed, F. & Hernández-Orallo, J. Trends in AI inference energy consumption: Beyond the performance-vs-parameter laws of deep learning. Sustainable Computing: Informatics and Systems 38, 100857 (2023) . (10) Xu, X. et al. Quantization of Fully Convolutional Networks for Accurate Biomedical Image Segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2018) . (11) Gou, J., Yu, B., Maybank, S. J. & Tao, D. Knowledge Distillation: A Survey. International Journal of Computer Vision 129 (6), 1789–1819 (2021) . (12) Ding, C. et al. REQ-YOLO: A Resource-Aware, Efficient Quantization Framework for Object Detection on FPGAs (2019). ArXiv:1909.13396 [cs]. (13) Kozlov, A., Lazarevich, I., Shamporov, V., Lyalyushkin, N. & Gorbachev, Y. Neural Network Compression Framework for fast model inference (2020). ArXiv:2002.08679 [cs, eess]. (14) Li, Y. et al. MQBench: Towards Reproducible and Deployable Model Quantization Benchmark. Neural Information Processing Systems (2021) . (15) Siddegowda, S. et al. Neural Network Quantization with AI Model Efficiency Toolkit (AIMET) (2022). ArXiv:2201.08442 [cs.LG]. (16) Microsoft. Neural Network Intelligence (2022). URL https://github.com/microsoft/nni. (17) AskariHemmat, M. et al. U-Net Fixed-Point Quantization for Medical Image Segmentation. Large-Scale Annotation of Biomedical Data and Expert Label Synthesis and Hardware Aware Learning for Medical Imaging and Computer Assisted Intervention 115–124 (2019) . (18) Micikevicius, P. et al. FP8 Formats for Deep Learning (2022). ArXiv:2209.05433 [cs]. (19) Han, S., Pool, J., Tran, J. & Dally, W. J. Learning both Weights and Connections for Efficient Neural Networks (2015). ArXiv:1506.02626 [cs] . (20) He, Y., Liu, P., Wang, Z., Hu, Z. & Yang, Y. Filter Pruning via Geometric Median for Deep Convolutional Neural Networks Acceleration (2018). ArXiv:1811.00250 [cs] . (21) Weigert, M. et al. Content-aware image restoration: pushing the limits of fluorescence microscopy. Nature Methods 15 (12), 1090–1097 (2018) . (22) Cardoso, M. J. et al. MONAI: An open-source framework for deep learning in healthcare (2022). ArXiv:2211.02701 [cs] . (23) Cardiff, R. D., Miller, C. H. & Munn, R. J. Manual Hematoxylin and Eosin Staining of Mouse Tissue Sections. Cold Spring Harbor Protocols 2014 (6), pdb.prot073411 (2014) . (24) Tomasovic, A. et al. Interference with ERK-dimerization at the nucleocytosolic interface targets pathological ERK1/2 signaling without cardiotoxic side-effects. Nature Communications 11 (1), 1733 (2020) . (25) Lorenz, K., Schmitt, J. P., Schmitteckert, E. M. & Lohse, M. J. A new type of ERK1/2 autophosphorylation causes cardiac hypertrophy. Nature Medicine 15 (1), 75–83 (2009) . (26) Grüneboom, A. et al. A network of trans-cortical capillaries as mainstay for blood circulation in long bones. Nature Metabolism 1 (2), 236–250 (2019) . (27) Ounkomol, C., Seshamani, S., Maleckar, M. M., Collman, F. & Johnson, G. R. Label-free prediction of three-dimensional fluorescence images from transmitted-light microscopy. Nature Methods 15 (11), 917–920 (2018) . (28) Viana, M. P. et al. Integrated intracellular organization and its variations in human iPS cells. Nature 613 (7943), 345–354 (2023) . Pachitariu, M. & Stringer, C. Cellpose 2.0: how to train your own model. Nature Methods 19 (12), 1634–1641 (2022) . (7) Sonneck, J. & Chen, J. MMV_im2im: An Open Source Microscopy Machine Vision Toolbox for Image-to-Image Transformation (2023). ArXiv:2209.02498 [cs]. (8) Mahecic, D. et al. Event-driven acquisition for content-enriched microscopy. Nature Methods 19 (10), 1262–1267 (2022) . (9) Desislavov, R., Martínez-Plumed, F. & Hernández-Orallo, J. Trends in AI inference energy consumption: Beyond the performance-vs-parameter laws of deep learning. Sustainable Computing: Informatics and Systems 38, 100857 (2023) . (10) Xu, X. et al. Quantization of Fully Convolutional Networks for Accurate Biomedical Image Segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2018) . (11) Gou, J., Yu, B., Maybank, S. J. & Tao, D. Knowledge Distillation: A Survey. International Journal of Computer Vision 129 (6), 1789–1819 (2021) . (12) Ding, C. et al. REQ-YOLO: A Resource-Aware, Efficient Quantization Framework for Object Detection on FPGAs (2019). ArXiv:1909.13396 [cs]. (13) Kozlov, A., Lazarevich, I., Shamporov, V., Lyalyushkin, N. & Gorbachev, Y. Neural Network Compression Framework for fast model inference (2020). ArXiv:2002.08679 [cs, eess]. (14) Li, Y. et al. MQBench: Towards Reproducible and Deployable Model Quantization Benchmark. Neural Information Processing Systems (2021) . (15) Siddegowda, S. et al. Neural Network Quantization with AI Model Efficiency Toolkit (AIMET) (2022). ArXiv:2201.08442 [cs.LG]. (16) Microsoft. Neural Network Intelligence (2022). URL https://github.com/microsoft/nni. (17) AskariHemmat, M. et al. U-Net Fixed-Point Quantization for Medical Image Segmentation. Large-Scale Annotation of Biomedical Data and Expert Label Synthesis and Hardware Aware Learning for Medical Imaging and Computer Assisted Intervention 115–124 (2019) . (18) Micikevicius, P. et al. FP8 Formats for Deep Learning (2022). ArXiv:2209.05433 [cs]. (19) Han, S., Pool, J., Tran, J. & Dally, W. J. Learning both Weights and Connections for Efficient Neural Networks (2015). ArXiv:1506.02626 [cs] . (20) He, Y., Liu, P., Wang, Z., Hu, Z. & Yang, Y. Filter Pruning via Geometric Median for Deep Convolutional Neural Networks Acceleration (2018). ArXiv:1811.00250 [cs] . (21) Weigert, M. et al. Content-aware image restoration: pushing the limits of fluorescence microscopy. Nature Methods 15 (12), 1090–1097 (2018) . (22) Cardoso, M. J. et al. MONAI: An open-source framework for deep learning in healthcare (2022). ArXiv:2211.02701 [cs] . (23) Cardiff, R. D., Miller, C. H. & Munn, R. J. Manual Hematoxylin and Eosin Staining of Mouse Tissue Sections. Cold Spring Harbor Protocols 2014 (6), pdb.prot073411 (2014) . (24) Tomasovic, A. et al. Interference with ERK-dimerization at the nucleocytosolic interface targets pathological ERK1/2 signaling without cardiotoxic side-effects. Nature Communications 11 (1), 1733 (2020) . (25) Lorenz, K., Schmitt, J. P., Schmitteckert, E. M. & Lohse, M. J. A new type of ERK1/2 autophosphorylation causes cardiac hypertrophy. Nature Medicine 15 (1), 75–83 (2009) . (26) Grüneboom, A. et al. A network of trans-cortical capillaries as mainstay for blood circulation in long bones. Nature Metabolism 1 (2), 236–250 (2019) . (27) Ounkomol, C., Seshamani, S., Maleckar, M. M., Collman, F. & Johnson, G. R. Label-free prediction of three-dimensional fluorescence images from transmitted-light microscopy. Nature Methods 15 (11), 917–920 (2018) . (28) Viana, M. P. et al. Integrated intracellular organization and its variations in human iPS cells. Nature 613 (7943), 345–354 (2023) . Sonneck, J. & Chen, J. MMV_im2im: An Open Source Microscopy Machine Vision Toolbox for Image-to-Image Transformation (2023). ArXiv:2209.02498 [cs]. (8) Mahecic, D. et al. Event-driven acquisition for content-enriched microscopy. Nature Methods 19 (10), 1262–1267 (2022) . (9) Desislavov, R., Martínez-Plumed, F. & Hernández-Orallo, J. Trends in AI inference energy consumption: Beyond the performance-vs-parameter laws of deep learning. Sustainable Computing: Informatics and Systems 38, 100857 (2023) . (10) Xu, X. et al. Quantization of Fully Convolutional Networks for Accurate Biomedical Image Segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2018) . (11) Gou, J., Yu, B., Maybank, S. J. & Tao, D. Knowledge Distillation: A Survey. International Journal of Computer Vision 129 (6), 1789–1819 (2021) . (12) Ding, C. et al. REQ-YOLO: A Resource-Aware, Efficient Quantization Framework for Object Detection on FPGAs (2019). ArXiv:1909.13396 [cs]. (13) Kozlov, A., Lazarevich, I., Shamporov, V., Lyalyushkin, N. & Gorbachev, Y. Neural Network Compression Framework for fast model inference (2020). ArXiv:2002.08679 [cs, eess]. (14) Li, Y. et al. MQBench: Towards Reproducible and Deployable Model Quantization Benchmark. Neural Information Processing Systems (2021) . (15) Siddegowda, S. et al. Neural Network Quantization with AI Model Efficiency Toolkit (AIMET) (2022). ArXiv:2201.08442 [cs.LG]. (16) Microsoft. Neural Network Intelligence (2022). URL https://github.com/microsoft/nni. (17) AskariHemmat, M. et al. U-Net Fixed-Point Quantization for Medical Image Segmentation. Large-Scale Annotation of Biomedical Data and Expert Label Synthesis and Hardware Aware Learning for Medical Imaging and Computer Assisted Intervention 115–124 (2019) . (18) Micikevicius, P. et al. FP8 Formats for Deep Learning (2022). ArXiv:2209.05433 [cs]. (19) Han, S., Pool, J., Tran, J. & Dally, W. J. Learning both Weights and Connections for Efficient Neural Networks (2015). ArXiv:1506.02626 [cs] . (20) He, Y., Liu, P., Wang, Z., Hu, Z. & Yang, Y. Filter Pruning via Geometric Median for Deep Convolutional Neural Networks Acceleration (2018). ArXiv:1811.00250 [cs] . (21) Weigert, M. et al. Content-aware image restoration: pushing the limits of fluorescence microscopy. Nature Methods 15 (12), 1090–1097 (2018) . (22) Cardoso, M. J. et al. MONAI: An open-source framework for deep learning in healthcare (2022). ArXiv:2211.02701 [cs] . (23) Cardiff, R. D., Miller, C. H. & Munn, R. J. Manual Hematoxylin and Eosin Staining of Mouse Tissue Sections. Cold Spring Harbor Protocols 2014 (6), pdb.prot073411 (2014) . (24) Tomasovic, A. et al. Interference with ERK-dimerization at the nucleocytosolic interface targets pathological ERK1/2 signaling without cardiotoxic side-effects. Nature Communications 11 (1), 1733 (2020) . (25) Lorenz, K., Schmitt, J. P., Schmitteckert, E. M. & Lohse, M. J. A new type of ERK1/2 autophosphorylation causes cardiac hypertrophy. Nature Medicine 15 (1), 75–83 (2009) . (26) Grüneboom, A. et al. A network of trans-cortical capillaries as mainstay for blood circulation in long bones. Nature Metabolism 1 (2), 236–250 (2019) . (27) Ounkomol, C., Seshamani, S., Maleckar, M. M., Collman, F. & Johnson, G. R. Label-free prediction of three-dimensional fluorescence images from transmitted-light microscopy. Nature Methods 15 (11), 917–920 (2018) . (28) Viana, M. P. et al. Integrated intracellular organization and its variations in human iPS cells. Nature 613 (7943), 345–354 (2023) . Mahecic, D. et al. Event-driven acquisition for content-enriched microscopy. Nature Methods 19 (10), 1262–1267 (2022) . (9) Desislavov, R., Martínez-Plumed, F. & Hernández-Orallo, J. Trends in AI inference energy consumption: Beyond the performance-vs-parameter laws of deep learning. Sustainable Computing: Informatics and Systems 38, 100857 (2023) . (10) Xu, X. et al. Quantization of Fully Convolutional Networks for Accurate Biomedical Image Segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2018) . (11) Gou, J., Yu, B., Maybank, S. J. & Tao, D. Knowledge Distillation: A Survey. International Journal of Computer Vision 129 (6), 1789–1819 (2021) . (12) Ding, C. et al. REQ-YOLO: A Resource-Aware, Efficient Quantization Framework for Object Detection on FPGAs (2019). ArXiv:1909.13396 [cs]. (13) Kozlov, A., Lazarevich, I., Shamporov, V., Lyalyushkin, N. & Gorbachev, Y. Neural Network Compression Framework for fast model inference (2020). ArXiv:2002.08679 [cs, eess]. (14) Li, Y. et al. MQBench: Towards Reproducible and Deployable Model Quantization Benchmark. Neural Information Processing Systems (2021) . (15) Siddegowda, S. et al. Neural Network Quantization with AI Model Efficiency Toolkit (AIMET) (2022). ArXiv:2201.08442 [cs.LG]. (16) Microsoft. Neural Network Intelligence (2022). URL https://github.com/microsoft/nni. (17) AskariHemmat, M. et al. U-Net Fixed-Point Quantization for Medical Image Segmentation. Large-Scale Annotation of Biomedical Data and Expert Label Synthesis and Hardware Aware Learning for Medical Imaging and Computer Assisted Intervention 115–124 (2019) . (18) Micikevicius, P. et al. FP8 Formats for Deep Learning (2022). ArXiv:2209.05433 [cs]. (19) Han, S., Pool, J., Tran, J. & Dally, W. J. Learning both Weights and Connections for Efficient Neural Networks (2015). ArXiv:1506.02626 [cs] . (20) He, Y., Liu, P., Wang, Z., Hu, Z. & Yang, Y. Filter Pruning via Geometric Median for Deep Convolutional Neural Networks Acceleration (2018). ArXiv:1811.00250 [cs] . (21) Weigert, M. et al. Content-aware image restoration: pushing the limits of fluorescence microscopy. Nature Methods 15 (12), 1090–1097 (2018) . (22) Cardoso, M. J. et al. MONAI: An open-source framework for deep learning in healthcare (2022). ArXiv:2211.02701 [cs] . (23) Cardiff, R. D., Miller, C. H. & Munn, R. J. Manual Hematoxylin and Eosin Staining of Mouse Tissue Sections. Cold Spring Harbor Protocols 2014 (6), pdb.prot073411 (2014) . (24) Tomasovic, A. et al. Interference with ERK-dimerization at the nucleocytosolic interface targets pathological ERK1/2 signaling without cardiotoxic side-effects. Nature Communications 11 (1), 1733 (2020) . (25) Lorenz, K., Schmitt, J. P., Schmitteckert, E. M. & Lohse, M. J. A new type of ERK1/2 autophosphorylation causes cardiac hypertrophy. Nature Medicine 15 (1), 75–83 (2009) . (26) Grüneboom, A. et al. A network of trans-cortical capillaries as mainstay for blood circulation in long bones. Nature Metabolism 1 (2), 236–250 (2019) . (27) Ounkomol, C., Seshamani, S., Maleckar, M. M., Collman, F. & Johnson, G. R. Label-free prediction of three-dimensional fluorescence images from transmitted-light microscopy. Nature Methods 15 (11), 917–920 (2018) . (28) Viana, M. P. et al. Integrated intracellular organization and its variations in human iPS cells. Nature 613 (7943), 345–354 (2023) . Desislavov, R., Martínez-Plumed, F. & Hernández-Orallo, J. Trends in AI inference energy consumption: Beyond the performance-vs-parameter laws of deep learning. Sustainable Computing: Informatics and Systems 38, 100857 (2023) . (10) Xu, X. et al. Quantization of Fully Convolutional Networks for Accurate Biomedical Image Segmentation. 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Manual Hematoxylin and Eosin Staining of Mouse Tissue Sections. Cold Spring Harbor Protocols 2014 (6), pdb.prot073411 (2014) . (24) Tomasovic, A. et al. Interference with ERK-dimerization at the nucleocytosolic interface targets pathological ERK1/2 signaling without cardiotoxic side-effects. Nature Communications 11 (1), 1733 (2020) . (25) Lorenz, K., Schmitt, J. P., Schmitteckert, E. M. & Lohse, M. J. A new type of ERK1/2 autophosphorylation causes cardiac hypertrophy. Nature Medicine 15 (1), 75–83 (2009) . (26) Grüneboom, A. et al. A network of trans-cortical capillaries as mainstay for blood circulation in long bones. Nature Metabolism 1 (2), 236–250 (2019) . (27) Ounkomol, C., Seshamani, S., Maleckar, M. M., Collman, F. & Johnson, G. R. Label-free prediction of three-dimensional fluorescence images from transmitted-light microscopy. Nature Methods 15 (11), 917–920 (2018) . (28) Viana, M. P. et al. Integrated intracellular organization and its variations in human iPS cells. 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(27) Ounkomol, C., Seshamani, S., Maleckar, M. M., Collman, F. & Johnson, G. R. Label-free prediction of three-dimensional fluorescence images from transmitted-light microscopy. Nature Methods 15 (11), 917–920 (2018) . (28) Viana, M. P. et al. Integrated intracellular organization and its variations in human iPS cells. Nature 613 (7943), 345–354 (2023) . Microsoft. Neural Network Intelligence (2022). URL https://github.com/microsoft/nni. (17) AskariHemmat, M. et al. U-Net Fixed-Point Quantization for Medical Image Segmentation. Large-Scale Annotation of Biomedical Data and Expert Label Synthesis and Hardware Aware Learning for Medical Imaging and Computer Assisted Intervention 115–124 (2019) . (18) Micikevicius, P. et al. FP8 Formats for Deep Learning (2022). ArXiv:2209.05433 [cs]. (19) Han, S., Pool, J., Tran, J. & Dally, W. J. Learning both Weights and Connections for Efficient Neural Networks (2015). ArXiv:1506.02626 [cs] . (20) He, Y., Liu, P., Wang, Z., Hu, Z. & Yang, Y. 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A network of trans-cortical capillaries as mainstay for blood circulation in long bones. Nature Metabolism 1 (2), 236–250 (2019) . (27) Ounkomol, C., Seshamani, S., Maleckar, M. M., Collman, F. & Johnson, G. R. Label-free prediction of three-dimensional fluorescence images from transmitted-light microscopy. Nature Methods 15 (11), 917–920 (2018) . (28) Viana, M. P. et al. Integrated intracellular organization and its variations in human iPS cells. Nature 613 (7943), 345–354 (2023) . AskariHemmat, M. et al. U-Net Fixed-Point Quantization for Medical Image Segmentation. Large-Scale Annotation of Biomedical Data and Expert Label Synthesis and Hardware Aware Learning for Medical Imaging and Computer Assisted Intervention 115–124 (2019) . (18) Micikevicius, P. et al. FP8 Formats for Deep Learning (2022). ArXiv:2209.05433 [cs]. (19) Han, S., Pool, J., Tran, J. & Dally, W. J. Learning both Weights and Connections for Efficient Neural Networks (2015). ArXiv:1506.02626 [cs] . 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(27) Ounkomol, C., Seshamani, S., Maleckar, M. M., Collman, F. & Johnson, G. R. Label-free prediction of three-dimensional fluorescence images from transmitted-light microscopy. Nature Methods 15 (11), 917–920 (2018) . (28) Viana, M. P. et al. Integrated intracellular organization and its variations in human iPS cells. Nature 613 (7943), 345–354 (2023) . Han, S., Pool, J., Tran, J. & Dally, W. J. Learning both Weights and Connections for Efficient Neural Networks (2015). ArXiv:1506.02626 [cs] . (20) He, Y., Liu, P., Wang, Z., Hu, Z. & Yang, Y. Filter Pruning via Geometric Median for Deep Convolutional Neural Networks Acceleration (2018). ArXiv:1811.00250 [cs] . (21) Weigert, M. et al. Content-aware image restoration: pushing the limits of fluorescence microscopy. Nature Methods 15 (12), 1090–1097 (2018) . (22) Cardoso, M. J. et al. MONAI: An open-source framework for deep learning in healthcare (2022). ArXiv:2211.02701 [cs] . (23) Cardiff, R. D., Miller, C. H. & Munn, R. J. Manual Hematoxylin and Eosin Staining of Mouse Tissue Sections. Cold Spring Harbor Protocols 2014 (6), pdb.prot073411 (2014) . (24) Tomasovic, A. et al. Interference with ERK-dimerization at the nucleocytosolic interface targets pathological ERK1/2 signaling without cardiotoxic side-effects. Nature Communications 11 (1), 1733 (2020) . (25) Lorenz, K., Schmitt, J. P., Schmitteckert, E. M. & Lohse, M. J. A new type of ERK1/2 autophosphorylation causes cardiac hypertrophy. Nature Medicine 15 (1), 75–83 (2009) . (26) Grüneboom, A. et al. A network of trans-cortical capillaries as mainstay for blood circulation in long bones. Nature Metabolism 1 (2), 236–250 (2019) . (27) Ounkomol, C., Seshamani, S., Maleckar, M. M., Collman, F. & Johnson, G. R. Label-free prediction of three-dimensional fluorescence images from transmitted-light microscopy. Nature Methods 15 (11), 917–920 (2018) . (28) Viana, M. P. et al. Integrated intracellular organization and its variations in human iPS cells. 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Label-free prediction of three-dimensional fluorescence images from transmitted-light microscopy. Nature Methods 15 (11), 917–920 (2018) . (28) Viana, M. P. et al. Integrated intracellular organization and its variations in human iPS cells. Nature 613 (7943), 345–354 (2023) . Tomasovic, A. et al. Interference with ERK-dimerization at the nucleocytosolic interface targets pathological ERK1/2 signaling without cardiotoxic side-effects. Nature Communications 11 (1), 1733 (2020) . (25) Lorenz, K., Schmitt, J. P., Schmitteckert, E. M. & Lohse, M. J. A new type of ERK1/2 autophosphorylation causes cardiac hypertrophy. Nature Medicine 15 (1), 75–83 (2009) . (26) Grüneboom, A. et al. A network of trans-cortical capillaries as mainstay for blood circulation in long bones. Nature Metabolism 1 (2), 236–250 (2019) . (27) Ounkomol, C., Seshamani, S., Maleckar, M. M., Collman, F. & Johnson, G. R. Label-free prediction of three-dimensional fluorescence images from transmitted-light microscopy. 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Nature Methods 15 (11), 917–920 (2018) . (28) Viana, M. P. et al. Integrated intracellular organization and its variations in human iPS cells. Nature 613 (7943), 345–354 (2023) . Gómez-de Mariscal, E. et al. DeepImageJ: A user-friendly environment to run deep learning models in ImageJ. Nature Methods 18 (10), 1192–1195 (2021) . (5) von Chamier, L. et al. Democratising deep learning for microscopy with ZeroCostDL4Mic. Nature Communications 12 (1), 2276 (2021) . (6) Pachitariu, M. & Stringer, C. Cellpose 2.0: how to train your own model. Nature Methods 19 (12), 1634–1641 (2022) . (7) Sonneck, J. & Chen, J. MMV_im2im: An Open Source Microscopy Machine Vision Toolbox for Image-to-Image Transformation (2023). ArXiv:2209.02498 [cs]. (8) Mahecic, D. et al. Event-driven acquisition for content-enriched microscopy. Nature Methods 19 (10), 1262–1267 (2022) . (9) Desislavov, R., Martínez-Plumed, F. & Hernández-Orallo, J. 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Label-free prediction of three-dimensional fluorescence images from transmitted-light microscopy. Nature Methods 15 (11), 917–920 (2018) . (28) Viana, M. P. et al. Integrated intracellular organization and its variations in human iPS cells. Nature 613 (7943), 345–354 (2023) . von Chamier, L. et al. Democratising deep learning for microscopy with ZeroCostDL4Mic. Nature Communications 12 (1), 2276 (2021) . (6) Pachitariu, M. & Stringer, C. Cellpose 2.0: how to train your own model. Nature Methods 19 (12), 1634–1641 (2022) . (7) Sonneck, J. & Chen, J. MMV_im2im: An Open Source Microscopy Machine Vision Toolbox for Image-to-Image Transformation (2023). ArXiv:2209.02498 [cs]. (8) Mahecic, D. et al. Event-driven acquisition for content-enriched microscopy. Nature Methods 19 (10), 1262–1267 (2022) . (9) Desislavov, R., Martínez-Plumed, F. & Hernández-Orallo, J. Trends in AI inference energy consumption: Beyond the performance-vs-parameter laws of deep learning. 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ArXiv:2201.08442 [cs.LG]. (16) Microsoft. Neural Network Intelligence (2022). URL https://github.com/microsoft/nni. (17) AskariHemmat, M. et al. U-Net Fixed-Point Quantization for Medical Image Segmentation. Large-Scale Annotation of Biomedical Data and Expert Label Synthesis and Hardware Aware Learning for Medical Imaging and Computer Assisted Intervention 115–124 (2019) . (18) Micikevicius, P. et al. FP8 Formats for Deep Learning (2022). ArXiv:2209.05433 [cs]. (19) Han, S., Pool, J., Tran, J. & Dally, W. J. Learning both Weights and Connections for Efficient Neural Networks (2015). ArXiv:1506.02626 [cs] . (20) He, Y., Liu, P., Wang, Z., Hu, Z. & Yang, Y. Filter Pruning via Geometric Median for Deep Convolutional Neural Networks Acceleration (2018). ArXiv:1811.00250 [cs] . (21) Weigert, M. et al. Content-aware image restoration: pushing the limits of fluorescence microscopy. Nature Methods 15 (12), 1090–1097 (2018) . (22) Cardoso, M. J. et al. 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Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2018) . (11) Gou, J., Yu, B., Maybank, S. J. & Tao, D. Knowledge Distillation: A Survey. International Journal of Computer Vision 129 (6), 1789–1819 (2021) . (12) Ding, C. et al. REQ-YOLO: A Resource-Aware, Efficient Quantization Framework for Object Detection on FPGAs (2019). ArXiv:1909.13396 [cs]. (13) Kozlov, A., Lazarevich, I., Shamporov, V., Lyalyushkin, N. & Gorbachev, Y. Neural Network Compression Framework for fast model inference (2020). ArXiv:2002.08679 [cs, eess]. (14) Li, Y. et al. MQBench: Towards Reproducible and Deployable Model Quantization Benchmark. Neural Information Processing Systems (2021) . (15) Siddegowda, S. et al. Neural Network Quantization with AI Model Efficiency Toolkit (AIMET) (2022). ArXiv:2201.08442 [cs.LG]. (16) Microsoft. Neural Network Intelligence (2022). URL https://github.com/microsoft/nni. (17) AskariHemmat, M. et al. 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Manual Hematoxylin and Eosin Staining of Mouse Tissue Sections. Cold Spring Harbor Protocols 2014 (6), pdb.prot073411 (2014) . (24) Tomasovic, A. et al. Interference with ERK-dimerization at the nucleocytosolic interface targets pathological ERK1/2 signaling without cardiotoxic side-effects. Nature Communications 11 (1), 1733 (2020) . (25) Lorenz, K., Schmitt, J. P., Schmitteckert, E. M. & Lohse, M. J. A new type of ERK1/2 autophosphorylation causes cardiac hypertrophy. Nature Medicine 15 (1), 75–83 (2009) . (26) Grüneboom, A. et al. A network of trans-cortical capillaries as mainstay for blood circulation in long bones. Nature Metabolism 1 (2), 236–250 (2019) . (27) Ounkomol, C., Seshamani, S., Maleckar, M. M., Collman, F. & Johnson, G. R. Label-free prediction of three-dimensional fluorescence images from transmitted-light microscopy. Nature Methods 15 (11), 917–920 (2018) . (28) Viana, M. P. et al. Integrated intracellular organization and its variations in human iPS cells. Nature 613 (7943), 345–354 (2023) . Sonneck, J. & Chen, J. MMV_im2im: An Open Source Microscopy Machine Vision Toolbox for Image-to-Image Transformation (2023). ArXiv:2209.02498 [cs]. (8) Mahecic, D. et al. Event-driven acquisition for content-enriched microscopy. Nature Methods 19 (10), 1262–1267 (2022) . (9) Desislavov, R., Martínez-Plumed, F. & Hernández-Orallo, J. Trends in AI inference energy consumption: Beyond the performance-vs-parameter laws of deep learning. Sustainable Computing: Informatics and Systems 38, 100857 (2023) . (10) Xu, X. et al. Quantization of Fully Convolutional Networks for Accurate Biomedical Image Segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2018) . (11) Gou, J., Yu, B., Maybank, S. J. & Tao, D. Knowledge Distillation: A Survey. International Journal of Computer Vision 129 (6), 1789–1819 (2021) . (12) Ding, C. et al. REQ-YOLO: A Resource-Aware, Efficient Quantization Framework for Object Detection on FPGAs (2019). ArXiv:1909.13396 [cs]. (13) Kozlov, A., Lazarevich, I., Shamporov, V., Lyalyushkin, N. & Gorbachev, Y. Neural Network Compression Framework for fast model inference (2020). ArXiv:2002.08679 [cs, eess]. (14) Li, Y. et al. MQBench: Towards Reproducible and Deployable Model Quantization Benchmark. Neural Information Processing Systems (2021) . (15) Siddegowda, S. et al. Neural Network Quantization with AI Model Efficiency Toolkit (AIMET) (2022). ArXiv:2201.08442 [cs.LG]. (16) Microsoft. Neural Network Intelligence (2022). URL https://github.com/microsoft/nni. (17) AskariHemmat, M. et al. U-Net Fixed-Point Quantization for Medical Image Segmentation. Large-Scale Annotation of Biomedical Data and Expert Label Synthesis and Hardware Aware Learning for Medical Imaging and Computer Assisted Intervention 115–124 (2019) . (18) Micikevicius, P. et al. FP8 Formats for Deep Learning (2022). ArXiv:2209.05433 [cs]. (19) Han, S., Pool, J., Tran, J. & Dally, W. J. Learning both Weights and Connections for Efficient Neural Networks (2015). ArXiv:1506.02626 [cs] . (20) He, Y., Liu, P., Wang, Z., Hu, Z. & Yang, Y. Filter Pruning via Geometric Median for Deep Convolutional Neural Networks Acceleration (2018). ArXiv:1811.00250 [cs] . (21) Weigert, M. et al. Content-aware image restoration: pushing the limits of fluorescence microscopy. Nature Methods 15 (12), 1090–1097 (2018) . (22) Cardoso, M. J. et al. MONAI: An open-source framework for deep learning in healthcare (2022). ArXiv:2211.02701 [cs] . (23) Cardiff, R. D., Miller, C. H. & Munn, R. J. Manual Hematoxylin and Eosin Staining of Mouse Tissue Sections. Cold Spring Harbor Protocols 2014 (6), pdb.prot073411 (2014) . (24) Tomasovic, A. et al. Interference with ERK-dimerization at the nucleocytosolic interface targets pathological ERK1/2 signaling without cardiotoxic side-effects. Nature Communications 11 (1), 1733 (2020) . (25) Lorenz, K., Schmitt, J. P., Schmitteckert, E. M. & Lohse, M. J. A new type of ERK1/2 autophosphorylation causes cardiac hypertrophy. Nature Medicine 15 (1), 75–83 (2009) . (26) Grüneboom, A. et al. A network of trans-cortical capillaries as mainstay for blood circulation in long bones. Nature Metabolism 1 (2), 236–250 (2019) . (27) Ounkomol, C., Seshamani, S., Maleckar, M. M., Collman, F. & Johnson, G. R. Label-free prediction of three-dimensional fluorescence images from transmitted-light microscopy. Nature Methods 15 (11), 917–920 (2018) . (28) Viana, M. P. et al. Integrated intracellular organization and its variations in human iPS cells. Nature 613 (7943), 345–354 (2023) . Mahecic, D. et al. Event-driven acquisition for content-enriched microscopy. Nature Methods 19 (10), 1262–1267 (2022) . (9) Desislavov, R., Martínez-Plumed, F. & Hernández-Orallo, J. Trends in AI inference energy consumption: Beyond the performance-vs-parameter laws of deep learning. Sustainable Computing: Informatics and Systems 38, 100857 (2023) . (10) Xu, X. et al. Quantization of Fully Convolutional Networks for Accurate Biomedical Image Segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2018) . (11) Gou, J., Yu, B., Maybank, S. J. & Tao, D. Knowledge Distillation: A Survey. International Journal of Computer Vision 129 (6), 1789–1819 (2021) . (12) Ding, C. et al. REQ-YOLO: A Resource-Aware, Efficient Quantization Framework for Object Detection on FPGAs (2019). ArXiv:1909.13396 [cs]. (13) Kozlov, A., Lazarevich, I., Shamporov, V., Lyalyushkin, N. & Gorbachev, Y. Neural Network Compression Framework for fast model inference (2020). ArXiv:2002.08679 [cs, eess]. (14) Li, Y. et al. MQBench: Towards Reproducible and Deployable Model Quantization Benchmark. Neural Information Processing Systems (2021) . (15) Siddegowda, S. et al. Neural Network Quantization with AI Model Efficiency Toolkit (AIMET) (2022). ArXiv:2201.08442 [cs.LG]. (16) Microsoft. Neural Network Intelligence (2022). URL https://github.com/microsoft/nni. (17) AskariHemmat, M. et al. U-Net Fixed-Point Quantization for Medical Image Segmentation. Large-Scale Annotation of Biomedical Data and Expert Label Synthesis and Hardware Aware Learning for Medical Imaging and Computer Assisted Intervention 115–124 (2019) . (18) Micikevicius, P. et al. FP8 Formats for Deep Learning (2022). ArXiv:2209.05433 [cs]. (19) Han, S., Pool, J., Tran, J. & Dally, W. J. Learning both Weights and Connections for Efficient Neural Networks (2015). ArXiv:1506.02626 [cs] . (20) He, Y., Liu, P., Wang, Z., Hu, Z. & Yang, Y. Filter Pruning via Geometric Median for Deep Convolutional Neural Networks Acceleration (2018). ArXiv:1811.00250 [cs] . (21) Weigert, M. et al. Content-aware image restoration: pushing the limits of fluorescence microscopy. Nature Methods 15 (12), 1090–1097 (2018) . (22) Cardoso, M. J. et al. 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Nature Methods 15 (11), 917–920 (2018) . (28) Viana, M. P. et al. Integrated intracellular organization and its variations in human iPS cells. Nature 613 (7943), 345–354 (2023) . Desislavov, R., Martínez-Plumed, F. & Hernández-Orallo, J. Trends in AI inference energy consumption: Beyond the performance-vs-parameter laws of deep learning. Sustainable Computing: Informatics and Systems 38, 100857 (2023) . (10) Xu, X. et al. Quantization of Fully Convolutional Networks for Accurate Biomedical Image Segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2018) . (11) Gou, J., Yu, B., Maybank, S. J. & Tao, D. Knowledge Distillation: A Survey. International Journal of Computer Vision 129 (6), 1789–1819 (2021) . (12) Ding, C. et al. REQ-YOLO: A Resource-Aware, Efficient Quantization Framework for Object Detection on FPGAs (2019). ArXiv:1909.13396 [cs]. (13) Kozlov, A., Lazarevich, I., Shamporov, V., Lyalyushkin, N. & Gorbachev, Y. Neural Network Compression Framework for fast model inference (2020). ArXiv:2002.08679 [cs, eess]. (14) Li, Y. et al. MQBench: Towards Reproducible and Deployable Model Quantization Benchmark. Neural Information Processing Systems (2021) . (15) Siddegowda, S. et al. Neural Network Quantization with AI Model Efficiency Toolkit (AIMET) (2022). ArXiv:2201.08442 [cs.LG]. (16) Microsoft. Neural Network Intelligence (2022). URL https://github.com/microsoft/nni. (17) AskariHemmat, M. et al. U-Net Fixed-Point Quantization for Medical Image Segmentation. Large-Scale Annotation of Biomedical Data and Expert Label Synthesis and Hardware Aware Learning for Medical Imaging and Computer Assisted Intervention 115–124 (2019) . (18) Micikevicius, P. et al. FP8 Formats for Deep Learning (2022). ArXiv:2209.05433 [cs]. (19) Han, S., Pool, J., Tran, J. & Dally, W. J. Learning both Weights and Connections for Efficient Neural Networks (2015). ArXiv:1506.02626 [cs] . 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Nature Medicine 15 (1), 75–83 (2009) . (26) Grüneboom, A. et al. A network of trans-cortical capillaries as mainstay for blood circulation in long bones. Nature Metabolism 1 (2), 236–250 (2019) . (27) Ounkomol, C., Seshamani, S., Maleckar, M. M., Collman, F. & Johnson, G. R. Label-free prediction of three-dimensional fluorescence images from transmitted-light microscopy. Nature Methods 15 (11), 917–920 (2018) . (28) Viana, M. P. et al. Integrated intracellular organization and its variations in human iPS cells. Nature 613 (7943), 345–354 (2023) . Xu, X. et al. Quantization of Fully Convolutional Networks for Accurate Biomedical Image Segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2018) . (11) Gou, J., Yu, B., Maybank, S. J. & Tao, D. Knowledge Distillation: A Survey. International Journal of Computer Vision 129 (6), 1789–1819 (2021) . (12) Ding, C. et al. REQ-YOLO: A Resource-Aware, Efficient Quantization Framework for Object Detection on FPGAs (2019). ArXiv:1909.13396 [cs]. (13) Kozlov, A., Lazarevich, I., Shamporov, V., Lyalyushkin, N. & Gorbachev, Y. Neural Network Compression Framework for fast model inference (2020). ArXiv:2002.08679 [cs, eess]. (14) Li, Y. et al. MQBench: Towards Reproducible and Deployable Model Quantization Benchmark. Neural Information Processing Systems (2021) . (15) Siddegowda, S. et al. Neural Network Quantization with AI Model Efficiency Toolkit (AIMET) (2022). ArXiv:2201.08442 [cs.LG]. (16) Microsoft. Neural Network Intelligence (2022). URL https://github.com/microsoft/nni. (17) AskariHemmat, M. et al. U-Net Fixed-Point Quantization for Medical Image Segmentation. Large-Scale Annotation of Biomedical Data and Expert Label Synthesis and Hardware Aware Learning for Medical Imaging and Computer Assisted Intervention 115–124 (2019) . (18) Micikevicius, P. et al. FP8 Formats for Deep Learning (2022). ArXiv:2209.05433 [cs]. (19) Han, S., Pool, J., Tran, J. & Dally, W. J. Learning both Weights and Connections for Efficient Neural Networks (2015). ArXiv:1506.02626 [cs] . (20) He, Y., Liu, P., Wang, Z., Hu, Z. & Yang, Y. Filter Pruning via Geometric Median for Deep Convolutional Neural Networks Acceleration (2018). ArXiv:1811.00250 [cs] . (21) Weigert, M. et al. Content-aware image restoration: pushing the limits of fluorescence microscopy. Nature Methods 15 (12), 1090–1097 (2018) . (22) Cardoso, M. J. et al. MONAI: An open-source framework for deep learning in healthcare (2022). ArXiv:2211.02701 [cs] . (23) Cardiff, R. D., Miller, C. H. & Munn, R. J. Manual Hematoxylin and Eosin Staining of Mouse Tissue Sections. Cold Spring Harbor Protocols 2014 (6), pdb.prot073411 (2014) . (24) Tomasovic, A. et al. Interference with ERK-dimerization at the nucleocytosolic interface targets pathological ERK1/2 signaling without cardiotoxic side-effects. Nature Communications 11 (1), 1733 (2020) . (25) Lorenz, K., Schmitt, J. P., Schmitteckert, E. M. & Lohse, M. J. A new type of ERK1/2 autophosphorylation causes cardiac hypertrophy. Nature Medicine 15 (1), 75–83 (2009) . (26) Grüneboom, A. et al. A network of trans-cortical capillaries as mainstay for blood circulation in long bones. Nature Metabolism 1 (2), 236–250 (2019) . (27) Ounkomol, C., Seshamani, S., Maleckar, M. M., Collman, F. & Johnson, G. R. Label-free prediction of three-dimensional fluorescence images from transmitted-light microscopy. Nature Methods 15 (11), 917–920 (2018) . (28) Viana, M. P. et al. Integrated intracellular organization and its variations in human iPS cells. Nature 613 (7943), 345–354 (2023) . Gou, J., Yu, B., Maybank, S. J. & Tao, D. Knowledge Distillation: A Survey. International Journal of Computer Vision 129 (6), 1789–1819 (2021) . (12) Ding, C. et al. REQ-YOLO: A Resource-Aware, Efficient Quantization Framework for Object Detection on FPGAs (2019). ArXiv:1909.13396 [cs]. (13) Kozlov, A., Lazarevich, I., Shamporov, V., Lyalyushkin, N. & Gorbachev, Y. Neural Network Compression Framework for fast model inference (2020). ArXiv:2002.08679 [cs, eess]. (14) Li, Y. et al. MQBench: Towards Reproducible and Deployable Model Quantization Benchmark. Neural Information Processing Systems (2021) . (15) Siddegowda, S. et al. Neural Network Quantization with AI Model Efficiency Toolkit (AIMET) (2022). ArXiv:2201.08442 [cs.LG]. (16) Microsoft. Neural Network Intelligence (2022). URL https://github.com/microsoft/nni. (17) AskariHemmat, M. et al. U-Net Fixed-Point Quantization for Medical Image Segmentation. Large-Scale Annotation of Biomedical Data and Expert Label Synthesis and Hardware Aware Learning for Medical Imaging and Computer Assisted Intervention 115–124 (2019) . (18) Micikevicius, P. et al. FP8 Formats for Deep Learning (2022). ArXiv:2209.05433 [cs]. (19) Han, S., Pool, J., Tran, J. & Dally, W. J. Learning both Weights and Connections for Efficient Neural Networks (2015). ArXiv:1506.02626 [cs] . (20) He, Y., Liu, P., Wang, Z., Hu, Z. & Yang, Y. Filter Pruning via Geometric Median for Deep Convolutional Neural Networks Acceleration (2018). ArXiv:1811.00250 [cs] . (21) Weigert, M. et al. Content-aware image restoration: pushing the limits of fluorescence microscopy. Nature Methods 15 (12), 1090–1097 (2018) . (22) Cardoso, M. J. et al. MONAI: An open-source framework for deep learning in healthcare (2022). ArXiv:2211.02701 [cs] . (23) Cardiff, R. D., Miller, C. H. & Munn, R. J. Manual Hematoxylin and Eosin Staining of Mouse Tissue Sections. Cold Spring Harbor Protocols 2014 (6), pdb.prot073411 (2014) . (24) Tomasovic, A. et al. Interference with ERK-dimerization at the nucleocytosolic interface targets pathological ERK1/2 signaling without cardiotoxic side-effects. Nature Communications 11 (1), 1733 (2020) . (25) Lorenz, K., Schmitt, J. P., Schmitteckert, E. M. & Lohse, M. J. A new type of ERK1/2 autophosphorylation causes cardiac hypertrophy. Nature Medicine 15 (1), 75–83 (2009) . (26) Grüneboom, A. et al. A network of trans-cortical capillaries as mainstay for blood circulation in long bones. Nature Metabolism 1 (2), 236–250 (2019) . (27) Ounkomol, C., Seshamani, S., Maleckar, M. M., Collman, F. & Johnson, G. R. Label-free prediction of three-dimensional fluorescence images from transmitted-light microscopy. Nature Methods 15 (11), 917–920 (2018) . (28) Viana, M. P. et al. Integrated intracellular organization and its variations in human iPS cells. Nature 613 (7943), 345–354 (2023) . Ding, C. et al. REQ-YOLO: A Resource-Aware, Efficient Quantization Framework for Object Detection on FPGAs (2019). ArXiv:1909.13396 [cs]. (13) Kozlov, A., Lazarevich, I., Shamporov, V., Lyalyushkin, N. & Gorbachev, Y. Neural Network Compression Framework for fast model inference (2020). ArXiv:2002.08679 [cs, eess]. (14) Li, Y. et al. MQBench: Towards Reproducible and Deployable Model Quantization Benchmark. Neural Information Processing Systems (2021) . (15) Siddegowda, S. et al. Neural Network Quantization with AI Model Efficiency Toolkit (AIMET) (2022). ArXiv:2201.08442 [cs.LG]. (16) Microsoft. Neural Network Intelligence (2022). URL https://github.com/microsoft/nni. (17) AskariHemmat, M. et al. U-Net Fixed-Point Quantization for Medical Image Segmentation. Large-Scale Annotation of Biomedical Data and Expert Label Synthesis and Hardware Aware Learning for Medical Imaging and Computer Assisted Intervention 115–124 (2019) . (18) Micikevicius, P. et al. FP8 Formats for Deep Learning (2022). ArXiv:2209.05433 [cs]. (19) Han, S., Pool, J., Tran, J. & Dally, W. J. Learning both Weights and Connections for Efficient Neural Networks (2015). ArXiv:1506.02626 [cs] . (20) He, Y., Liu, P., Wang, Z., Hu, Z. & Yang, Y. Filter Pruning via Geometric Median for Deep Convolutional Neural Networks Acceleration (2018). ArXiv:1811.00250 [cs] . (21) Weigert, M. et al. Content-aware image restoration: pushing the limits of fluorescence microscopy. Nature Methods 15 (12), 1090–1097 (2018) . (22) Cardoso, M. J. et al. MONAI: An open-source framework for deep learning in healthcare (2022). ArXiv:2211.02701 [cs] . (23) Cardiff, R. D., Miller, C. H. & Munn, R. J. Manual Hematoxylin and Eosin Staining of Mouse Tissue Sections. Cold Spring Harbor Protocols 2014 (6), pdb.prot073411 (2014) . (24) Tomasovic, A. et al. Interference with ERK-dimerization at the nucleocytosolic interface targets pathological ERK1/2 signaling without cardiotoxic side-effects. Nature Communications 11 (1), 1733 (2020) . (25) Lorenz, K., Schmitt, J. P., Schmitteckert, E. M. & Lohse, M. J. A new type of ERK1/2 autophosphorylation causes cardiac hypertrophy. Nature Medicine 15 (1), 75–83 (2009) . (26) Grüneboom, A. et al. A network of trans-cortical capillaries as mainstay for blood circulation in long bones. 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Cold Spring Harbor Protocols 2014 (6), pdb.prot073411 (2014) . (24) Tomasovic, A. et al. Interference with ERK-dimerization at the nucleocytosolic interface targets pathological ERK1/2 signaling without cardiotoxic side-effects. Nature Communications 11 (1), 1733 (2020) . (25) Lorenz, K., Schmitt, J. P., Schmitteckert, E. M. & Lohse, M. J. A new type of ERK1/2 autophosphorylation causes cardiac hypertrophy. Nature Medicine 15 (1), 75–83 (2009) . (26) Grüneboom, A. et al. A network of trans-cortical capillaries as mainstay for blood circulation in long bones. Nature Metabolism 1 (2), 236–250 (2019) . (27) Ounkomol, C., Seshamani, S., Maleckar, M. M., Collman, F. & Johnson, G. R. Label-free prediction of three-dimensional fluorescence images from transmitted-light microscopy. Nature Methods 15 (11), 917–920 (2018) . (28) Viana, M. P. et al. Integrated intracellular organization and its variations in human iPS cells. Nature 613 (7943), 345–354 (2023) . Sonneck, J. & Chen, J. 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(23) Cardiff, R. D., Miller, C. H. & Munn, R. J. Manual Hematoxylin and Eosin Staining of Mouse Tissue Sections. Cold Spring Harbor Protocols 2014 (6), pdb.prot073411 (2014) . (24) Tomasovic, A. et al. Interference with ERK-dimerization at the nucleocytosolic interface targets pathological ERK1/2 signaling without cardiotoxic side-effects. Nature Communications 11 (1), 1733 (2020) . (25) Lorenz, K., Schmitt, J. P., Schmitteckert, E. M. & Lohse, M. J. A new type of ERK1/2 autophosphorylation causes cardiac hypertrophy. Nature Medicine 15 (1), 75–83 (2009) . (26) Grüneboom, A. et al. A network of trans-cortical capillaries as mainstay for blood circulation in long bones. Nature Metabolism 1 (2), 236–250 (2019) . (27) Ounkomol, C., Seshamani, S., Maleckar, M. M., Collman, F. & Johnson, G. R. Label-free prediction of three-dimensional fluorescence images from transmitted-light microscopy. Nature Methods 15 (11), 917–920 (2018) . (28) Viana, M. P. et al. Integrated intracellular organization and its variations in human iPS cells. Nature 613 (7943), 345–354 (2023) . Desislavov, R., Martínez-Plumed, F. & Hernández-Orallo, J. Trends in AI inference energy consumption: Beyond the performance-vs-parameter laws of deep learning. Sustainable Computing: Informatics and Systems 38, 100857 (2023) . (10) Xu, X. et al. Quantization of Fully Convolutional Networks for Accurate Biomedical Image Segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2018) . (11) Gou, J., Yu, B., Maybank, S. J. & Tao, D. Knowledge Distillation: A Survey. International Journal of Computer Vision 129 (6), 1789–1819 (2021) . (12) Ding, C. et al. REQ-YOLO: A Resource-Aware, Efficient Quantization Framework for Object Detection on FPGAs (2019). ArXiv:1909.13396 [cs]. (13) Kozlov, A., Lazarevich, I., Shamporov, V., Lyalyushkin, N. & Gorbachev, Y. Neural Network Compression Framework for fast model inference (2020). ArXiv:2002.08679 [cs, eess]. (14) Li, Y. et al. MQBench: Towards Reproducible and Deployable Model Quantization Benchmark. Neural Information Processing Systems (2021) . (15) Siddegowda, S. et al. Neural Network Quantization with AI Model Efficiency Toolkit (AIMET) (2022). ArXiv:2201.08442 [cs.LG]. (16) Microsoft. Neural Network Intelligence (2022). URL https://github.com/microsoft/nni. (17) AskariHemmat, M. et al. U-Net Fixed-Point Quantization for Medical Image Segmentation. Large-Scale Annotation of Biomedical Data and Expert Label Synthesis and Hardware Aware Learning for Medical Imaging and Computer Assisted Intervention 115–124 (2019) . (18) Micikevicius, P. et al. FP8 Formats for Deep Learning (2022). ArXiv:2209.05433 [cs]. (19) Han, S., Pool, J., Tran, J. & Dally, W. J. Learning both Weights and Connections for Efficient Neural Networks (2015). ArXiv:1506.02626 [cs] . (20) He, Y., Liu, P., Wang, Z., Hu, Z. & Yang, Y. Filter Pruning via Geometric Median for Deep Convolutional Neural Networks Acceleration (2018). ArXiv:1811.00250 [cs] . (21) Weigert, M. et al. Content-aware image restoration: pushing the limits of fluorescence microscopy. Nature Methods 15 (12), 1090–1097 (2018) . (22) Cardoso, M. J. et al. MONAI: An open-source framework for deep learning in healthcare (2022). ArXiv:2211.02701 [cs] . (23) Cardiff, R. D., Miller, C. H. & Munn, R. J. Manual Hematoxylin and Eosin Staining of Mouse Tissue Sections. Cold Spring Harbor Protocols 2014 (6), pdb.prot073411 (2014) . (24) Tomasovic, A. et al. Interference with ERK-dimerization at the nucleocytosolic interface targets pathological ERK1/2 signaling without cardiotoxic side-effects. Nature Communications 11 (1), 1733 (2020) . (25) Lorenz, K., Schmitt, J. P., Schmitteckert, E. M. & Lohse, M. J. A new type of ERK1/2 autophosphorylation causes cardiac hypertrophy. Nature Medicine 15 (1), 75–83 (2009) . (26) Grüneboom, A. et al. A network of trans-cortical capillaries as mainstay for blood circulation in long bones. Nature Metabolism 1 (2), 236–250 (2019) . (27) Ounkomol, C., Seshamani, S., Maleckar, M. M., Collman, F. & Johnson, G. R. Label-free prediction of three-dimensional fluorescence images from transmitted-light microscopy. Nature Methods 15 (11), 917–920 (2018) . (28) Viana, M. P. et al. Integrated intracellular organization and its variations in human iPS cells. Nature 613 (7943), 345–354 (2023) . Xu, X. et al. Quantization of Fully Convolutional Networks for Accurate Biomedical Image Segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2018) . (11) Gou, J., Yu, B., Maybank, S. J. & Tao, D. Knowledge Distillation: A Survey. International Journal of Computer Vision 129 (6), 1789–1819 (2021) . (12) Ding, C. et al. REQ-YOLO: A Resource-Aware, Efficient Quantization Framework for Object Detection on FPGAs (2019). ArXiv:1909.13396 [cs]. (13) Kozlov, A., Lazarevich, I., Shamporov, V., Lyalyushkin, N. & Gorbachev, Y. Neural Network Compression Framework for fast model inference (2020). ArXiv:2002.08679 [cs, eess]. (14) Li, Y. et al. MQBench: Towards Reproducible and Deployable Model Quantization Benchmark. Neural Information Processing Systems (2021) . (15) Siddegowda, S. et al. Neural Network Quantization with AI Model Efficiency Toolkit (AIMET) (2022). ArXiv:2201.08442 [cs.LG]. (16) Microsoft. Neural Network Intelligence (2022). URL https://github.com/microsoft/nni. (17) AskariHemmat, M. et al. U-Net Fixed-Point Quantization for Medical Image Segmentation. Large-Scale Annotation of Biomedical Data and Expert Label Synthesis and Hardware Aware Learning for Medical Imaging and Computer Assisted Intervention 115–124 (2019) . (18) Micikevicius, P. et al. FP8 Formats for Deep Learning (2022). ArXiv:2209.05433 [cs]. (19) Han, S., Pool, J., Tran, J. & Dally, W. J. 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(19) Han, S., Pool, J., Tran, J. & Dally, W. J. Learning both Weights and Connections for Efficient Neural Networks (2015). ArXiv:1506.02626 [cs] . (20) He, Y., Liu, P., Wang, Z., Hu, Z. & Yang, Y. Filter Pruning via Geometric Median for Deep Convolutional Neural Networks Acceleration (2018). ArXiv:1811.00250 [cs] . (21) Weigert, M. et al. Content-aware image restoration: pushing the limits of fluorescence microscopy. Nature Methods 15 (12), 1090–1097 (2018) . (22) Cardoso, M. J. et al. MONAI: An open-source framework for deep learning in healthcare (2022). ArXiv:2211.02701 [cs] . (23) Cardiff, R. D., Miller, C. H. & Munn, R. J. Manual Hematoxylin and Eosin Staining of Mouse Tissue Sections. Cold Spring Harbor Protocols 2014 (6), pdb.prot073411 (2014) . (24) Tomasovic, A. et al. Interference with ERK-dimerization at the nucleocytosolic interface targets pathological ERK1/2 signaling without cardiotoxic side-effects. Nature Communications 11 (1), 1733 (2020) . (25) Lorenz, K., Schmitt, J. P., Schmitteckert, E. M. & Lohse, M. J. A new type of ERK1/2 autophosphorylation causes cardiac hypertrophy. Nature Medicine 15 (1), 75–83 (2009) . (26) Grüneboom, A. et al. A network of trans-cortical capillaries as mainstay for blood circulation in long bones. Nature Metabolism 1 (2), 236–250 (2019) . (27) Ounkomol, C., Seshamani, S., Maleckar, M. M., Collman, F. & Johnson, G. R. Label-free prediction of three-dimensional fluorescence images from transmitted-light microscopy. Nature Methods 15 (11), 917–920 (2018) . (28) Viana, M. P. et al. Integrated intracellular organization and its variations in human iPS cells. Nature 613 (7943), 345–354 (2023) . Microsoft. Neural Network Intelligence (2022). URL https://github.com/microsoft/nni. (17) AskariHemmat, M. et al. U-Net Fixed-Point Quantization for Medical Image Segmentation. 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Nature 613 (7943), 345–354 (2023) . Micikevicius, P. et al. FP8 Formats for Deep Learning (2022). ArXiv:2209.05433 [cs]. (19) Han, S., Pool, J., Tran, J. & Dally, W. J. Learning both Weights and Connections for Efficient Neural Networks (2015). ArXiv:1506.02626 [cs] . (20) He, Y., Liu, P., Wang, Z., Hu, Z. & Yang, Y. Filter Pruning via Geometric Median for Deep Convolutional Neural Networks Acceleration (2018). ArXiv:1811.00250 [cs] . (21) Weigert, M. et al. Content-aware image restoration: pushing the limits of fluorescence microscopy. Nature Methods 15 (12), 1090–1097 (2018) . (22) Cardoso, M. J. et al. MONAI: An open-source framework for deep learning in healthcare (2022). ArXiv:2211.02701 [cs] . (23) Cardiff, R. D., Miller, C. H. & Munn, R. J. Manual Hematoxylin and Eosin Staining of Mouse Tissue Sections. Cold Spring Harbor Protocols 2014 (6), pdb.prot073411 (2014) . (24) Tomasovic, A. et al. 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ArXiv:1506.02626 [cs] . (20) He, Y., Liu, P., Wang, Z., Hu, Z. & Yang, Y. Filter Pruning via Geometric Median for Deep Convolutional Neural Networks Acceleration (2018). ArXiv:1811.00250 [cs] . (21) Weigert, M. et al. Content-aware image restoration: pushing the limits of fluorescence microscopy. Nature Methods 15 (12), 1090–1097 (2018) . (22) Cardoso, M. J. et al. MONAI: An open-source framework for deep learning in healthcare (2022). ArXiv:2211.02701 [cs] . (23) Cardiff, R. D., Miller, C. H. & Munn, R. J. Manual Hematoxylin and Eosin Staining of Mouse Tissue Sections. Cold Spring Harbor Protocols 2014 (6), pdb.prot073411 (2014) . (24) Tomasovic, A. et al. Interference with ERK-dimerization at the nucleocytosolic interface targets pathological ERK1/2 signaling without cardiotoxic side-effects. Nature Communications 11 (1), 1733 (2020) . (25) Lorenz, K., Schmitt, J. P., Schmitteckert, E. M. & Lohse, M. J. A new type of ERK1/2 autophosphorylation causes cardiac hypertrophy. 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(23) Cardiff, R. D., Miller, C. H. & Munn, R. J. Manual Hematoxylin and Eosin Staining of Mouse Tissue Sections. Cold Spring Harbor Protocols 2014 (6), pdb.prot073411 (2014) . (24) Tomasovic, A. et al. Interference with ERK-dimerization at the nucleocytosolic interface targets pathological ERK1/2 signaling without cardiotoxic side-effects. Nature Communications 11 (1), 1733 (2020) . (25) Lorenz, K., Schmitt, J. P., Schmitteckert, E. M. & Lohse, M. J. A new type of ERK1/2 autophosphorylation causes cardiac hypertrophy. Nature Medicine 15 (1), 75–83 (2009) . (26) Grüneboom, A. et al. A network of trans-cortical capillaries as mainstay for blood circulation in long bones. Nature Metabolism 1 (2), 236–250 (2019) . (27) Ounkomol, C., Seshamani, S., Maleckar, M. M., Collman, F. & Johnson, G. R. Label-free prediction of three-dimensional fluorescence images from transmitted-light microscopy. Nature Methods 15 (11), 917–920 (2018) . (28) Viana, M. P. et al. 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(25) Lorenz, K., Schmitt, J. P., Schmitteckert, E. M. & Lohse, M. J. A new type of ERK1/2 autophosphorylation causes cardiac hypertrophy. Nature Medicine 15 (1), 75–83 (2009) . (26) Grüneboom, A. et al. A network of trans-cortical capillaries as mainstay for blood circulation in long bones. Nature Metabolism 1 (2), 236–250 (2019) . (27) Ounkomol, C., Seshamani, S., Maleckar, M. M., Collman, F. & Johnson, G. R. Label-free prediction of three-dimensional fluorescence images from transmitted-light microscopy. Nature Methods 15 (11), 917–920 (2018) . (28) Viana, M. P. et al. Integrated intracellular organization and its variations in human iPS cells. Nature 613 (7943), 345–354 (2023) . Sonneck, J. & Chen, J. MMV_im2im: An Open Source Microscopy Machine Vision Toolbox for Image-to-Image Transformation (2023). ArXiv:2209.02498 [cs]. (8) Mahecic, D. et al. Event-driven acquisition for content-enriched microscopy. Nature Methods 19 (10), 1262–1267 (2022) . (9) Desislavov, R., Martínez-Plumed, F. & Hernández-Orallo, J. Trends in AI inference energy consumption: Beyond the performance-vs-parameter laws of deep learning. Sustainable Computing: Informatics and Systems 38, 100857 (2023) . (10) Xu, X. et al. Quantization of Fully Convolutional Networks for Accurate Biomedical Image Segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2018) . (11) Gou, J., Yu, B., Maybank, S. J. & Tao, D. Knowledge Distillation: A Survey. International Journal of Computer Vision 129 (6), 1789–1819 (2021) . (12) Ding, C. et al. REQ-YOLO: A Resource-Aware, Efficient Quantization Framework for Object Detection on FPGAs (2019). ArXiv:1909.13396 [cs]. (13) Kozlov, A., Lazarevich, I., Shamporov, V., Lyalyushkin, N. & Gorbachev, Y. Neural Network Compression Framework for fast model inference (2020). ArXiv:2002.08679 [cs, eess]. (14) Li, Y. et al. MQBench: Towards Reproducible and Deployable Model Quantization Benchmark. 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Content-aware image restoration: pushing the limits of fluorescence microscopy. Nature Methods 15 (12), 1090–1097 (2018) . (22) Cardoso, M. J. et al. MONAI: An open-source framework for deep learning in healthcare (2022). ArXiv:2211.02701 [cs] . (23) Cardiff, R. D., Miller, C. H. & Munn, R. J. Manual Hematoxylin and Eosin Staining of Mouse Tissue Sections. Cold Spring Harbor Protocols 2014 (6), pdb.prot073411 (2014) . (24) Tomasovic, A. et al. Interference with ERK-dimerization at the nucleocytosolic interface targets pathological ERK1/2 signaling without cardiotoxic side-effects. Nature Communications 11 (1), 1733 (2020) . (25) Lorenz, K., Schmitt, J. P., Schmitteckert, E. M. & Lohse, M. J. A new type of ERK1/2 autophosphorylation causes cardiac hypertrophy. Nature Medicine 15 (1), 75–83 (2009) . (26) Grüneboom, A. et al. A network of trans-cortical capillaries as mainstay for blood circulation in long bones. Nature Metabolism 1 (2), 236–250 (2019) . (27) Ounkomol, C., Seshamani, S., Maleckar, M. M., Collman, F. & Johnson, G. R. Label-free prediction of three-dimensional fluorescence images from transmitted-light microscopy. Nature Methods 15 (11), 917–920 (2018) . (28) Viana, M. P. et al. Integrated intracellular organization and its variations in human iPS cells. Nature 613 (7943), 345–354 (2023) . Mahecic, D. et al. Event-driven acquisition for content-enriched microscopy. Nature Methods 19 (10), 1262–1267 (2022) . (9) Desislavov, R., Martínez-Plumed, F. & Hernández-Orallo, J. Trends in AI inference energy consumption: Beyond the performance-vs-parameter laws of deep learning. Sustainable Computing: Informatics and Systems 38, 100857 (2023) . (10) Xu, X. et al. Quantization of Fully Convolutional Networks for Accurate Biomedical Image Segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2018) . (11) Gou, J., Yu, B., Maybank, S. J. & Tao, D. Knowledge Distillation: A Survey. International Journal of Computer Vision 129 (6), 1789–1819 (2021) . (12) Ding, C. et al. REQ-YOLO: A Resource-Aware, Efficient Quantization Framework for Object Detection on FPGAs (2019). ArXiv:1909.13396 [cs]. (13) Kozlov, A., Lazarevich, I., Shamporov, V., Lyalyushkin, N. & Gorbachev, Y. Neural Network Compression Framework for fast model inference (2020). ArXiv:2002.08679 [cs, eess]. (14) Li, Y. et al. MQBench: Towards Reproducible and Deployable Model Quantization Benchmark. Neural Information Processing Systems (2021) . (15) Siddegowda, S. et al. Neural Network Quantization with AI Model Efficiency Toolkit (AIMET) (2022). ArXiv:2201.08442 [cs.LG]. (16) Microsoft. Neural Network Intelligence (2022). URL https://github.com/microsoft/nni. (17) AskariHemmat, M. et al. U-Net Fixed-Point Quantization for Medical Image Segmentation. Large-Scale Annotation of Biomedical Data and Expert Label Synthesis and Hardware Aware Learning for Medical Imaging and Computer Assisted Intervention 115–124 (2019) . (18) Micikevicius, P. et al. FP8 Formats for Deep Learning (2022). ArXiv:2209.05433 [cs]. (19) Han, S., Pool, J., Tran, J. & Dally, W. J. Learning both Weights and Connections for Efficient Neural Networks (2015). ArXiv:1506.02626 [cs] . (20) He, Y., Liu, P., Wang, Z., Hu, Z. & Yang, Y. Filter Pruning via Geometric Median for Deep Convolutional Neural Networks Acceleration (2018). ArXiv:1811.00250 [cs] . (21) Weigert, M. et al. Content-aware image restoration: pushing the limits of fluorescence microscopy. Nature Methods 15 (12), 1090–1097 (2018) . (22) Cardoso, M. J. et al. MONAI: An open-source framework for deep learning in healthcare (2022). ArXiv:2211.02701 [cs] . (23) Cardiff, R. D., Miller, C. H. & Munn, R. J. Manual Hematoxylin and Eosin Staining of Mouse Tissue Sections. Cold Spring Harbor Protocols 2014 (6), pdb.prot073411 (2014) . (24) Tomasovic, A. et al. Interference with ERK-dimerization at the nucleocytosolic interface targets pathological ERK1/2 signaling without cardiotoxic side-effects. Nature Communications 11 (1), 1733 (2020) . (25) Lorenz, K., Schmitt, J. P., Schmitteckert, E. M. & Lohse, M. J. A new type of ERK1/2 autophosphorylation causes cardiac hypertrophy. Nature Medicine 15 (1), 75–83 (2009) . (26) Grüneboom, A. et al. A network of trans-cortical capillaries as mainstay for blood circulation in long bones. Nature Metabolism 1 (2), 236–250 (2019) . (27) Ounkomol, C., Seshamani, S., Maleckar, M. M., Collman, F. & Johnson, G. R. Label-free prediction of three-dimensional fluorescence images from transmitted-light microscopy. Nature Methods 15 (11), 917–920 (2018) . (28) Viana, M. P. et al. Integrated intracellular organization and its variations in human iPS cells. Nature 613 (7943), 345–354 (2023) . Desislavov, R., Martínez-Plumed, F. & Hernández-Orallo, J. Trends in AI inference energy consumption: Beyond the performance-vs-parameter laws of deep learning. Sustainable Computing: Informatics and Systems 38, 100857 (2023) . (10) Xu, X. et al. Quantization of Fully Convolutional Networks for Accurate Biomedical Image Segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2018) . (11) Gou, J., Yu, B., Maybank, S. J. & Tao, D. Knowledge Distillation: A Survey. International Journal of Computer Vision 129 (6), 1789–1819 (2021) . (12) Ding, C. et al. REQ-YOLO: A Resource-Aware, Efficient Quantization Framework for Object Detection on FPGAs (2019). ArXiv:1909.13396 [cs]. (13) Kozlov, A., Lazarevich, I., Shamporov, V., Lyalyushkin, N. & Gorbachev, Y. Neural Network Compression Framework for fast model inference (2020). ArXiv:2002.08679 [cs, eess]. (14) Li, Y. et al. MQBench: Towards Reproducible and Deployable Model Quantization Benchmark. Neural Information Processing Systems (2021) . (15) Siddegowda, S. et al. Neural Network Quantization with AI Model Efficiency Toolkit (AIMET) (2022). ArXiv:2201.08442 [cs.LG]. (16) Microsoft. Neural Network Intelligence (2022). URL https://github.com/microsoft/nni. (17) AskariHemmat, M. et al. U-Net Fixed-Point Quantization for Medical Image Segmentation. Large-Scale Annotation of Biomedical Data and Expert Label Synthesis and Hardware Aware Learning for Medical Imaging and Computer Assisted Intervention 115–124 (2019) . (18) Micikevicius, P. et al. FP8 Formats for Deep Learning (2022). ArXiv:2209.05433 [cs]. (19) Han, S., Pool, J., Tran, J. & Dally, W. J. Learning both Weights and Connections for Efficient Neural Networks (2015). ArXiv:1506.02626 [cs] . (20) He, Y., Liu, P., Wang, Z., Hu, Z. & Yang, Y. Filter Pruning via Geometric Median for Deep Convolutional Neural Networks Acceleration (2018). ArXiv:1811.00250 [cs] . (21) Weigert, M. et al. Content-aware image restoration: pushing the limits of fluorescence microscopy. Nature Methods 15 (12), 1090–1097 (2018) . (22) Cardoso, M. J. et al. MONAI: An open-source framework for deep learning in healthcare (2022). ArXiv:2211.02701 [cs] . (23) Cardiff, R. D., Miller, C. H. & Munn, R. J. Manual Hematoxylin and Eosin Staining of Mouse Tissue Sections. Cold Spring Harbor Protocols 2014 (6), pdb.prot073411 (2014) . (24) Tomasovic, A. et al. Interference with ERK-dimerization at the nucleocytosolic interface targets pathological ERK1/2 signaling without cardiotoxic side-effects. Nature Communications 11 (1), 1733 (2020) . (25) Lorenz, K., Schmitt, J. P., Schmitteckert, E. M. & Lohse, M. J. A new type of ERK1/2 autophosphorylation causes cardiac hypertrophy. Nature Medicine 15 (1), 75–83 (2009) . (26) Grüneboom, A. et al. A network of trans-cortical capillaries as mainstay for blood circulation in long bones. Nature Metabolism 1 (2), 236–250 (2019) . 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(27) Ounkomol, C., Seshamani, S., Maleckar, M. M., Collman, F. & Johnson, G. R. Label-free prediction of three-dimensional fluorescence images from transmitted-light microscopy. Nature Methods 15 (11), 917–920 (2018) . (28) Viana, M. P. et al. Integrated intracellular organization and its variations in human iPS cells. Nature 613 (7943), 345–354 (2023) . Siddegowda, S. et al. Neural Network Quantization with AI Model Efficiency Toolkit (AIMET) (2022). ArXiv:2201.08442 [cs.LG]. (16) Microsoft. Neural Network Intelligence (2022). URL https://github.com/microsoft/nni. (17) AskariHemmat, M. et al. U-Net Fixed-Point Quantization for Medical Image Segmentation. Large-Scale Annotation of Biomedical Data and Expert Label Synthesis and Hardware Aware Learning for Medical Imaging and Computer Assisted Intervention 115–124 (2019) . (18) Micikevicius, P. et al. FP8 Formats for Deep Learning (2022). ArXiv:2209.05433 [cs]. (19) Han, S., Pool, J., Tran, J. & Dally, W. J. 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A new type of ERK1/2 autophosphorylation causes cardiac hypertrophy. Nature Medicine 15 (1), 75–83 (2009) . (26) Grüneboom, A. et al. A network of trans-cortical capillaries as mainstay for blood circulation in long bones. Nature Metabolism 1 (2), 236–250 (2019) . (27) Ounkomol, C., Seshamani, S., Maleckar, M. M., Collman, F. & Johnson, G. R. Label-free prediction of three-dimensional fluorescence images from transmitted-light microscopy. Nature Methods 15 (11), 917–920 (2018) . (28) Viana, M. P. et al. Integrated intracellular organization and its variations in human iPS cells. Nature 613 (7943), 345–354 (2023) . Microsoft. Neural Network Intelligence (2022). URL https://github.com/microsoft/nni. (17) AskariHemmat, M. et al. U-Net Fixed-Point Quantization for Medical Image Segmentation. Large-Scale Annotation of Biomedical Data and Expert Label Synthesis and Hardware Aware Learning for Medical Imaging and Computer Assisted Intervention 115–124 (2019) . (18) Micikevicius, P. et al. FP8 Formats for Deep Learning (2022). ArXiv:2209.05433 [cs]. (19) Han, S., Pool, J., Tran, J. & Dally, W. J. Learning both Weights and Connections for Efficient Neural Networks (2015). ArXiv:1506.02626 [cs] . (20) He, Y., Liu, P., Wang, Z., Hu, Z. & Yang, Y. Filter Pruning via Geometric Median for Deep Convolutional Neural Networks Acceleration (2018). ArXiv:1811.00250 [cs] . (21) Weigert, M. et al. Content-aware image restoration: pushing the limits of fluorescence microscopy. Nature Methods 15 (12), 1090–1097 (2018) . (22) Cardoso, M. J. et al. MONAI: An open-source framework for deep learning in healthcare (2022). ArXiv:2211.02701 [cs] . (23) Cardiff, R. D., Miller, C. H. & Munn, R. J. Manual Hematoxylin and Eosin Staining of Mouse Tissue Sections. Cold Spring Harbor Protocols 2014 (6), pdb.prot073411 (2014) . (24) Tomasovic, A. et al. Interference with ERK-dimerization at the nucleocytosolic interface targets pathological ERK1/2 signaling without cardiotoxic side-effects. Nature Communications 11 (1), 1733 (2020) . (25) Lorenz, K., Schmitt, J. P., Schmitteckert, E. M. & Lohse, M. J. A new type of ERK1/2 autophosphorylation causes cardiac hypertrophy. Nature Medicine 15 (1), 75–83 (2009) . (26) Grüneboom, A. et al. A network of trans-cortical capillaries as mainstay for blood circulation in long bones. Nature Metabolism 1 (2), 236–250 (2019) . (27) Ounkomol, C., Seshamani, S., Maleckar, M. M., Collman, F. & Johnson, G. R. Label-free prediction of three-dimensional fluorescence images from transmitted-light microscopy. Nature Methods 15 (11), 917–920 (2018) . (28) Viana, M. P. et al. Integrated intracellular organization and its variations in human iPS cells. Nature 613 (7943), 345–354 (2023) . AskariHemmat, M. et al. U-Net Fixed-Point Quantization for Medical Image Segmentation. Large-Scale Annotation of Biomedical Data and Expert Label Synthesis and Hardware Aware Learning for Medical Imaging and Computer Assisted Intervention 115–124 (2019) . (18) Micikevicius, P. et al. FP8 Formats for Deep Learning (2022). ArXiv:2209.05433 [cs]. (19) Han, S., Pool, J., Tran, J. & Dally, W. J. Learning both Weights and Connections for Efficient Neural Networks (2015). ArXiv:1506.02626 [cs] . (20) He, Y., Liu, P., Wang, Z., Hu, Z. & Yang, Y. Filter Pruning via Geometric Median for Deep Convolutional Neural Networks Acceleration (2018). ArXiv:1811.00250 [cs] . (21) Weigert, M. et al. Content-aware image restoration: pushing the limits of fluorescence microscopy. Nature Methods 15 (12), 1090–1097 (2018) . (22) Cardoso, M. J. et al. MONAI: An open-source framework for deep learning in healthcare (2022). ArXiv:2211.02701 [cs] . (23) Cardiff, R. D., Miller, C. H. & Munn, R. J. Manual Hematoxylin and Eosin Staining of Mouse Tissue Sections. Cold Spring Harbor Protocols 2014 (6), pdb.prot073411 (2014) . (24) Tomasovic, A. et al. 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(25) Lorenz, K., Schmitt, J. P., Schmitteckert, E. M. & Lohse, M. J. A new type of ERK1/2 autophosphorylation causes cardiac hypertrophy. Nature Medicine 15 (1), 75–83 (2009) . (26) Grüneboom, A. et al. A network of trans-cortical capillaries as mainstay for blood circulation in long bones. Nature Metabolism 1 (2), 236–250 (2019) . (27) Ounkomol, C., Seshamani, S., Maleckar, M. M., Collman, F. & Johnson, G. R. Label-free prediction of three-dimensional fluorescence images from transmitted-light microscopy. Nature Methods 15 (11), 917–920 (2018) . (28) Viana, M. P. et al. Integrated intracellular organization and its variations in human iPS cells. Nature 613 (7943), 345–354 (2023) . Han, S., Pool, J., Tran, J. & Dally, W. J. Learning both Weights and Connections for Efficient Neural Networks (2015). ArXiv:1506.02626 [cs] . (20) He, Y., Liu, P., Wang, Z., Hu, Z. & Yang, Y. Filter Pruning via Geometric Median for Deep Convolutional Neural Networks Acceleration (2018). ArXiv:1811.00250 [cs] . (21) Weigert, M. et al. Content-aware image restoration: pushing the limits of fluorescence microscopy. Nature Methods 15 (12), 1090–1097 (2018) . (22) Cardoso, M. J. et al. MONAI: An open-source framework for deep learning in healthcare (2022). ArXiv:2211.02701 [cs] . (23) Cardiff, R. D., Miller, C. H. & Munn, R. J. Manual Hematoxylin and Eosin Staining of Mouse Tissue Sections. Cold Spring Harbor Protocols 2014 (6), pdb.prot073411 (2014) . (24) Tomasovic, A. et al. Interference with ERK-dimerization at the nucleocytosolic interface targets pathological ERK1/2 signaling without cardiotoxic side-effects. Nature Communications 11 (1), 1733 (2020) . (25) Lorenz, K., Schmitt, J. P., Schmitteckert, E. M. & Lohse, M. J. A new type of ERK1/2 autophosphorylation causes cardiac hypertrophy. Nature Medicine 15 (1), 75–83 (2009) . (26) Grüneboom, A. et al. A network of trans-cortical capillaries as mainstay for blood circulation in long bones. Nature Metabolism 1 (2), 236–250 (2019) . (27) Ounkomol, C., Seshamani, S., Maleckar, M. M., Collman, F. & Johnson, G. R. Label-free prediction of three-dimensional fluorescence images from transmitted-light microscopy. Nature Methods 15 (11), 917–920 (2018) . (28) Viana, M. P. et al. Integrated intracellular organization and its variations in human iPS cells. Nature 613 (7943), 345–354 (2023) . He, Y., Liu, P., Wang, Z., Hu, Z. & Yang, Y. Filter Pruning via Geometric Median for Deep Convolutional Neural Networks Acceleration (2018). ArXiv:1811.00250 [cs] . (21) Weigert, M. et al. Content-aware image restoration: pushing the limits of fluorescence microscopy. Nature Methods 15 (12), 1090–1097 (2018) . (22) Cardoso, M. J. et al. MONAI: An open-source framework for deep learning in healthcare (2022). ArXiv:2211.02701 [cs] . (23) Cardiff, R. D., Miller, C. H. & Munn, R. J. Manual Hematoxylin and Eosin Staining of Mouse Tissue Sections. 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(27) Ounkomol, C., Seshamani, S., Maleckar, M. M., Collman, F. & Johnson, G. R. Label-free prediction of three-dimensional fluorescence images from transmitted-light microscopy. Nature Methods 15 (11), 917–920 (2018) . (28) Viana, M. P. et al. Integrated intracellular organization and its variations in human iPS cells. Nature 613 (7943), 345–354 (2023) . Cardoso, M. J. et al. MONAI: An open-source framework for deep learning in healthcare (2022). ArXiv:2211.02701 [cs] . (23) Cardiff, R. D., Miller, C. H. & Munn, R. J. Manual Hematoxylin and Eosin Staining of Mouse Tissue Sections. Cold Spring Harbor Protocols 2014 (6), pdb.prot073411 (2014) . (24) Tomasovic, A. et al. Interference with ERK-dimerization at the nucleocytosolic interface targets pathological ERK1/2 signaling without cardiotoxic side-effects. Nature Communications 11 (1), 1733 (2020) . (25) Lorenz, K., Schmitt, J. P., Schmitteckert, E. M. & Lohse, M. J. A new type of ERK1/2 autophosphorylation causes cardiac hypertrophy. 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(25) Lorenz, K., Schmitt, J. P., Schmitteckert, E. M. & Lohse, M. J. A new type of ERK1/2 autophosphorylation causes cardiac hypertrophy. Nature Medicine 15 (1), 75–83 (2009) . (26) Grüneboom, A. et al. A network of trans-cortical capillaries as mainstay for blood circulation in long bones. Nature Metabolism 1 (2), 236–250 (2019) . (27) Ounkomol, C., Seshamani, S., Maleckar, M. M., Collman, F. & Johnson, G. R. Label-free prediction of three-dimensional fluorescence images from transmitted-light microscopy. Nature Methods 15 (11), 917–920 (2018) . (28) Viana, M. P. et al. Integrated intracellular organization and its variations in human iPS cells. Nature 613 (7943), 345–354 (2023) . Tomasovic, A. et al. Interference with ERK-dimerization at the nucleocytosolic interface targets pathological ERK1/2 signaling without cardiotoxic side-effects. Nature Communications 11 (1), 1733 (2020) . (25) Lorenz, K., Schmitt, J. P., Schmitteckert, E. M. & Lohse, M. J. A new type of ERK1/2 autophosphorylation causes cardiac hypertrophy. Nature Medicine 15 (1), 75–83 (2009) . (26) Grüneboom, A. et al. A network of trans-cortical capillaries as mainstay for blood circulation in long bones. Nature Metabolism 1 (2), 236–250 (2019) . (27) Ounkomol, C., Seshamani, S., Maleckar, M. M., Collman, F. & Johnson, G. R. Label-free prediction of three-dimensional fluorescence images from transmitted-light microscopy. Nature Methods 15 (11), 917–920 (2018) . (28) Viana, M. P. et al. Integrated intracellular organization and its variations in human iPS cells. Nature 613 (7943), 345–354 (2023) . Lorenz, K., Schmitt, J. P., Schmitteckert, E. M. & Lohse, M. J. A new type of ERK1/2 autophosphorylation causes cardiac hypertrophy. Nature Medicine 15 (1), 75–83 (2009) . (26) Grüneboom, A. et al. A network of trans-cortical capillaries as mainstay for blood circulation in long bones. Nature Metabolism 1 (2), 236–250 (2019) . (27) Ounkomol, C., Seshamani, S., Maleckar, M. M., Collman, F. & Johnson, G. R. Label-free prediction of three-dimensional fluorescence images from transmitted-light microscopy. Nature Methods 15 (11), 917–920 (2018) . (28) Viana, M. P. et al. Integrated intracellular organization and its variations in human iPS cells. Nature 613 (7943), 345–354 (2023) . Grüneboom, A. et al. A network of trans-cortical capillaries as mainstay for blood circulation in long bones. Nature Metabolism 1 (2), 236–250 (2019) . (27) Ounkomol, C., Seshamani, S., Maleckar, M. M., Collman, F. & Johnson, G. R. Label-free prediction of three-dimensional fluorescence images from transmitted-light microscopy. Nature Methods 15 (11), 917–920 (2018) . (28) Viana, M. P. et al. Integrated intracellular organization and its variations in human iPS cells. Nature 613 (7943), 345–354 (2023) . Ounkomol, C., Seshamani, S., Maleckar, M. M., Collman, F. & Johnson, G. R. Label-free prediction of three-dimensional fluorescence images from transmitted-light microscopy. Nature Methods 15 (11), 917–920 (2018) . (28) Viana, M. P. et al. Integrated intracellular organization and its variations in human iPS cells. Nature 613 (7943), 345–354 (2023) . Viana, M. P. et al. Integrated intracellular organization and its variations in human iPS cells. Nature 613 (7943), 345–354 (2023) .
  6. Cellpose 2.0: how to train your own model. Nature Methods 19 (12), 1634–1641 (2022) . (7) Sonneck, J. & Chen, J. MMV_im2im: An Open Source Microscopy Machine Vision Toolbox for Image-to-Image Transformation (2023). ArXiv:2209.02498 [cs]. (8) Mahecic, D. et al. Event-driven acquisition for content-enriched microscopy. Nature Methods 19 (10), 1262–1267 (2022) . (9) Desislavov, R., Martínez-Plumed, F. & Hernández-Orallo, J. Trends in AI inference energy consumption: Beyond the performance-vs-parameter laws of deep learning. Sustainable Computing: Informatics and Systems 38, 100857 (2023) . (10) Xu, X. et al. Quantization of Fully Convolutional Networks for Accurate Biomedical Image Segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2018) . (11) Gou, J., Yu, B., Maybank, S. J. & Tao, D. Knowledge Distillation: A Survey. International Journal of Computer Vision 129 (6), 1789–1819 (2021) . (12) Ding, C. et al. 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(25) Lorenz, K., Schmitt, J. P., Schmitteckert, E. M. & Lohse, M. J. A new type of ERK1/2 autophosphorylation causes cardiac hypertrophy. Nature Medicine 15 (1), 75–83 (2009) . (26) Grüneboom, A. et al. A network of trans-cortical capillaries as mainstay for blood circulation in long bones. Nature Metabolism 1 (2), 236–250 (2019) . (27) Ounkomol, C., Seshamani, S., Maleckar, M. M., Collman, F. & Johnson, G. R. Label-free prediction of three-dimensional fluorescence images from transmitted-light microscopy. Nature Methods 15 (11), 917–920 (2018) . (28) Viana, M. P. et al. Integrated intracellular organization and its variations in human iPS cells. Nature 613 (7943), 345–354 (2023) . Sonneck, J. & Chen, J. MMV_im2im: An Open Source Microscopy Machine Vision Toolbox for Image-to-Image Transformation (2023). ArXiv:2209.02498 [cs]. (8) Mahecic, D. et al. Event-driven acquisition for content-enriched microscopy. Nature Methods 19 (10), 1262–1267 (2022) . (9) Desislavov, R., Martínez-Plumed, F. & Hernández-Orallo, J. Trends in AI inference energy consumption: Beyond the performance-vs-parameter laws of deep learning. Sustainable Computing: Informatics and Systems 38, 100857 (2023) . (10) Xu, X. et al. Quantization of Fully Convolutional Networks for Accurate Biomedical Image Segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2018) . (11) Gou, J., Yu, B., Maybank, S. J. & Tao, D. Knowledge Distillation: A Survey. International Journal of Computer Vision 129 (6), 1789–1819 (2021) . (12) Ding, C. et al. REQ-YOLO: A Resource-Aware, Efficient Quantization Framework for Object Detection on FPGAs (2019). ArXiv:1909.13396 [cs]. (13) Kozlov, A., Lazarevich, I., Shamporov, V., Lyalyushkin, N. & Gorbachev, Y. Neural Network Compression Framework for fast model inference (2020). ArXiv:2002.08679 [cs, eess]. (14) Li, Y. et al. MQBench: Towards Reproducible and Deployable Model Quantization Benchmark. Neural Information Processing Systems (2021) . (15) Siddegowda, S. et al. Neural Network Quantization with AI Model Efficiency Toolkit (AIMET) (2022). ArXiv:2201.08442 [cs.LG]. (16) Microsoft. Neural Network Intelligence (2022). URL https://github.com/microsoft/nni. (17) AskariHemmat, M. et al. U-Net Fixed-Point Quantization for Medical Image Segmentation. Large-Scale Annotation of Biomedical Data and Expert Label Synthesis and Hardware Aware Learning for Medical Imaging and Computer Assisted Intervention 115–124 (2019) . (18) Micikevicius, P. et al. FP8 Formats for Deep Learning (2022). ArXiv:2209.05433 [cs]. (19) Han, S., Pool, J., Tran, J. & Dally, W. J. Learning both Weights and Connections for Efficient Neural Networks (2015). ArXiv:1506.02626 [cs] . (20) He, Y., Liu, P., Wang, Z., Hu, Z. & Yang, Y. Filter Pruning via Geometric Median for Deep Convolutional Neural Networks Acceleration (2018). ArXiv:1811.00250 [cs] . (21) Weigert, M. et al. Content-aware image restoration: pushing the limits of fluorescence microscopy. Nature Methods 15 (12), 1090–1097 (2018) . (22) Cardoso, M. J. et al. MONAI: An open-source framework for deep learning in healthcare (2022). ArXiv:2211.02701 [cs] . (23) Cardiff, R. D., Miller, C. H. & Munn, R. J. Manual Hematoxylin and Eosin Staining of Mouse Tissue Sections. Cold Spring Harbor Protocols 2014 (6), pdb.prot073411 (2014) . (24) Tomasovic, A. et al. Interference with ERK-dimerization at the nucleocytosolic interface targets pathological ERK1/2 signaling without cardiotoxic side-effects. Nature Communications 11 (1), 1733 (2020) . (25) Lorenz, K., Schmitt, J. P., Schmitteckert, E. M. & Lohse, M. J. A new type of ERK1/2 autophosphorylation causes cardiac hypertrophy. Nature Medicine 15 (1), 75–83 (2009) . (26) Grüneboom, A. et al. A network of trans-cortical capillaries as mainstay for blood circulation in long bones. Nature Metabolism 1 (2), 236–250 (2019) . (27) Ounkomol, C., Seshamani, S., Maleckar, M. M., Collman, F. & Johnson, G. R. Label-free prediction of three-dimensional fluorescence images from transmitted-light microscopy. Nature Methods 15 (11), 917–920 (2018) . (28) Viana, M. P. et al. Integrated intracellular organization and its variations in human iPS cells. Nature 613 (7943), 345–354 (2023) . Mahecic, D. et al. Event-driven acquisition for content-enriched microscopy. Nature Methods 19 (10), 1262–1267 (2022) . (9) Desislavov, R., Martínez-Plumed, F. & Hernández-Orallo, J. Trends in AI inference energy consumption: Beyond the performance-vs-parameter laws of deep learning. Sustainable Computing: Informatics and Systems 38, 100857 (2023) . (10) Xu, X. et al. Quantization of Fully Convolutional Networks for Accurate Biomedical Image Segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2018) . (11) Gou, J., Yu, B., Maybank, S. J. & Tao, D. Knowledge Distillation: A Survey. International Journal of Computer Vision 129 (6), 1789–1819 (2021) . (12) Ding, C. et al. REQ-YOLO: A Resource-Aware, Efficient Quantization Framework for Object Detection on FPGAs (2019). ArXiv:1909.13396 [cs]. (13) Kozlov, A., Lazarevich, I., Shamporov, V., Lyalyushkin, N. & Gorbachev, Y. Neural Network Compression Framework for fast model inference (2020). ArXiv:2002.08679 [cs, eess]. (14) Li, Y. et al. MQBench: Towards Reproducible and Deployable Model Quantization Benchmark. Neural Information Processing Systems (2021) . (15) Siddegowda, S. et al. Neural Network Quantization with AI Model Efficiency Toolkit (AIMET) (2022). ArXiv:2201.08442 [cs.LG]. (16) Microsoft. Neural Network Intelligence (2022). URL https://github.com/microsoft/nni. (17) AskariHemmat, M. et al. U-Net Fixed-Point Quantization for Medical Image Segmentation. Large-Scale Annotation of Biomedical Data and Expert Label Synthesis and Hardware Aware Learning for Medical Imaging and Computer Assisted Intervention 115–124 (2019) . (18) Micikevicius, P. et al. FP8 Formats for Deep Learning (2022). ArXiv:2209.05433 [cs]. (19) Han, S., Pool, J., Tran, J. & Dally, W. J. Learning both Weights and Connections for Efficient Neural Networks (2015). ArXiv:1506.02626 [cs] . (20) He, Y., Liu, P., Wang, Z., Hu, Z. & Yang, Y. Filter Pruning via Geometric Median for Deep Convolutional Neural Networks Acceleration (2018). ArXiv:1811.00250 [cs] . (21) Weigert, M. et al. Content-aware image restoration: pushing the limits of fluorescence microscopy. Nature Methods 15 (12), 1090–1097 (2018) . (22) Cardoso, M. J. et al. MONAI: An open-source framework for deep learning in healthcare (2022). ArXiv:2211.02701 [cs] . (23) Cardiff, R. D., Miller, C. H. & Munn, R. J. Manual Hematoxylin and Eosin Staining of Mouse Tissue Sections. Cold Spring Harbor Protocols 2014 (6), pdb.prot073411 (2014) . (24) Tomasovic, A. et al. Interference with ERK-dimerization at the nucleocytosolic interface targets pathological ERK1/2 signaling without cardiotoxic side-effects. Nature Communications 11 (1), 1733 (2020) . (25) Lorenz, K., Schmitt, J. P., Schmitteckert, E. M. & Lohse, M. J. A new type of ERK1/2 autophosphorylation causes cardiac hypertrophy. Nature Medicine 15 (1), 75–83 (2009) . (26) Grüneboom, A. et al. A network of trans-cortical capillaries as mainstay for blood circulation in long bones. Nature Metabolism 1 (2), 236–250 (2019) . (27) Ounkomol, C., Seshamani, S., Maleckar, M. M., Collman, F. & Johnson, G. R. Label-free prediction of three-dimensional fluorescence images from transmitted-light microscopy. Nature Methods 15 (11), 917–920 (2018) . (28) Viana, M. P. et al. Integrated intracellular organization and its variations in human iPS cells. Nature 613 (7943), 345–354 (2023) . Desislavov, R., Martínez-Plumed, F. & Hernández-Orallo, J. Trends in AI inference energy consumption: Beyond the performance-vs-parameter laws of deep learning. Sustainable Computing: Informatics and Systems 38, 100857 (2023) . (10) Xu, X. et al. Quantization of Fully Convolutional Networks for Accurate Biomedical Image Segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2018) . (11) Gou, J., Yu, B., Maybank, S. J. & Tao, D. Knowledge Distillation: A Survey. International Journal of Computer Vision 129 (6), 1789–1819 (2021) . (12) Ding, C. et al. REQ-YOLO: A Resource-Aware, Efficient Quantization Framework for Object Detection on FPGAs (2019). ArXiv:1909.13396 [cs]. (13) Kozlov, A., Lazarevich, I., Shamporov, V., Lyalyushkin, N. & Gorbachev, Y. Neural Network Compression Framework for fast model inference (2020). ArXiv:2002.08679 [cs, eess]. (14) Li, Y. et al. MQBench: Towards Reproducible and Deployable Model Quantization Benchmark. Neural Information Processing Systems (2021) . (15) Siddegowda, S. et al. Neural Network Quantization with AI Model Efficiency Toolkit (AIMET) (2022). ArXiv:2201.08442 [cs.LG]. (16) Microsoft. Neural Network Intelligence (2022). URL https://github.com/microsoft/nni. (17) AskariHemmat, M. et al. U-Net Fixed-Point Quantization for Medical Image Segmentation. Large-Scale Annotation of Biomedical Data and Expert Label Synthesis and Hardware Aware Learning for Medical Imaging and Computer Assisted Intervention 115–124 (2019) . (18) Micikevicius, P. et al. FP8 Formats for Deep Learning (2022). ArXiv:2209.05433 [cs]. (19) Han, S., Pool, J., Tran, J. & Dally, W. J. Learning both Weights and Connections for Efficient Neural Networks (2015). ArXiv:1506.02626 [cs] . (20) He, Y., Liu, P., Wang, Z., Hu, Z. & Yang, Y. Filter Pruning via Geometric Median for Deep Convolutional Neural Networks Acceleration (2018). ArXiv:1811.00250 [cs] . (21) Weigert, M. et al. Content-aware image restoration: pushing the limits of fluorescence microscopy. Nature Methods 15 (12), 1090–1097 (2018) . (22) Cardoso, M. J. et al. MONAI: An open-source framework for deep learning in healthcare (2022). ArXiv:2211.02701 [cs] . (23) Cardiff, R. D., Miller, C. H. & Munn, R. J. Manual Hematoxylin and Eosin Staining of Mouse Tissue Sections. Cold Spring Harbor Protocols 2014 (6), pdb.prot073411 (2014) . (24) Tomasovic, A. et al. Interference with ERK-dimerization at the nucleocytosolic interface targets pathological ERK1/2 signaling without cardiotoxic side-effects. Nature Communications 11 (1), 1733 (2020) . (25) Lorenz, K., Schmitt, J. P., Schmitteckert, E. M. & Lohse, M. J. A new type of ERK1/2 autophosphorylation causes cardiac hypertrophy. Nature Medicine 15 (1), 75–83 (2009) . (26) Grüneboom, A. et al. A network of trans-cortical capillaries as mainstay for blood circulation in long bones. Nature Metabolism 1 (2), 236–250 (2019) . (27) Ounkomol, C., Seshamani, S., Maleckar, M. M., Collman, F. & Johnson, G. R. Label-free prediction of three-dimensional fluorescence images from transmitted-light microscopy. Nature Methods 15 (11), 917–920 (2018) . (28) Viana, M. P. et al. Integrated intracellular organization and its variations in human iPS cells. Nature 613 (7943), 345–354 (2023) . Xu, X. et al. Quantization of Fully Convolutional Networks for Accurate Biomedical Image Segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2018) . (11) Gou, J., Yu, B., Maybank, S. J. & Tao, D. Knowledge Distillation: A Survey. International Journal of Computer Vision 129 (6), 1789–1819 (2021) . (12) Ding, C. et al. REQ-YOLO: A Resource-Aware, Efficient Quantization Framework for Object Detection on FPGAs (2019). ArXiv:1909.13396 [cs]. (13) Kozlov, A., Lazarevich, I., Shamporov, V., Lyalyushkin, N. & Gorbachev, Y. Neural Network Compression Framework for fast model inference (2020). ArXiv:2002.08679 [cs, eess]. (14) Li, Y. et al. MQBench: Towards Reproducible and Deployable Model Quantization Benchmark. Neural Information Processing Systems (2021) . (15) Siddegowda, S. et al. Neural Network Quantization with AI Model Efficiency Toolkit (AIMET) (2022). ArXiv:2201.08442 [cs.LG]. (16) Microsoft. Neural Network Intelligence (2022). URL https://github.com/microsoft/nni. (17) AskariHemmat, M. et al. U-Net Fixed-Point Quantization for Medical Image Segmentation. Large-Scale Annotation of Biomedical Data and Expert Label Synthesis and Hardware Aware Learning for Medical Imaging and Computer Assisted Intervention 115–124 (2019) . (18) Micikevicius, P. et al. FP8 Formats for Deep Learning (2022). ArXiv:2209.05433 [cs]. (19) Han, S., Pool, J., Tran, J. & Dally, W. J. Learning both Weights and Connections for Efficient Neural Networks (2015). ArXiv:1506.02626 [cs] . (20) He, Y., Liu, P., Wang, Z., Hu, Z. & Yang, Y. 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(27) Ounkomol, C., Seshamani, S., Maleckar, M. M., Collman, F. & Johnson, G. R. Label-free prediction of three-dimensional fluorescence images from transmitted-light microscopy. Nature Methods 15 (11), 917–920 (2018) . (28) Viana, M. P. et al. Integrated intracellular organization and its variations in human iPS cells. Nature 613 (7943), 345–354 (2023) . Siddegowda, S. et al. Neural Network Quantization with AI Model Efficiency Toolkit (AIMET) (2022). ArXiv:2201.08442 [cs.LG]. (16) Microsoft. Neural Network Intelligence (2022). URL https://github.com/microsoft/nni. (17) AskariHemmat, M. et al. U-Net Fixed-Point Quantization for Medical Image Segmentation. Large-Scale Annotation of Biomedical Data and Expert Label Synthesis and Hardware Aware Learning for Medical Imaging and Computer Assisted Intervention 115–124 (2019) . (18) Micikevicius, P. et al. FP8 Formats for Deep Learning (2022). ArXiv:2209.05433 [cs]. (19) Han, S., Pool, J., Tran, J. & Dally, W. J. 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(25) Lorenz, K., Schmitt, J. P., Schmitteckert, E. M. & Lohse, M. J. A new type of ERK1/2 autophosphorylation causes cardiac hypertrophy. Nature Medicine 15 (1), 75–83 (2009) . (26) Grüneboom, A. et al. A network of trans-cortical capillaries as mainstay for blood circulation in long bones. Nature Metabolism 1 (2), 236–250 (2019) . (27) Ounkomol, C., Seshamani, S., Maleckar, M. M., Collman, F. & Johnson, G. R. Label-free prediction of three-dimensional fluorescence images from transmitted-light microscopy. Nature Methods 15 (11), 917–920 (2018) . (28) Viana, M. P. et al. Integrated intracellular organization and its variations in human iPS cells. Nature 613 (7943), 345–354 (2023) . Han, S., Pool, J., Tran, J. & Dally, W. J. Learning both Weights and Connections for Efficient Neural Networks (2015). ArXiv:1506.02626 [cs] . (20) He, Y., Liu, P., Wang, Z., Hu, Z. & Yang, Y. Filter Pruning via Geometric Median for Deep Convolutional Neural Networks Acceleration (2018). 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(25) Lorenz, K., Schmitt, J. P., Schmitteckert, E. M. & Lohse, M. J. A new type of ERK1/2 autophosphorylation causes cardiac hypertrophy. Nature Medicine 15 (1), 75–83 (2009) . (26) Grüneboom, A. et al. A network of trans-cortical capillaries as mainstay for blood circulation in long bones. Nature Metabolism 1 (2), 236–250 (2019) . (27) Ounkomol, C., Seshamani, S., Maleckar, M. M., Collman, F. & Johnson, G. R. Label-free prediction of three-dimensional fluorescence images from transmitted-light microscopy. Nature Methods 15 (11), 917–920 (2018) . (28) Viana, M. P. et al. Integrated intracellular organization and its variations in human iPS cells. Nature 613 (7943), 345–354 (2023) . Tomasovic, A. et al. Interference with ERK-dimerization at the nucleocytosolic interface targets pathological ERK1/2 signaling without cardiotoxic side-effects. Nature Communications 11 (1), 1733 (2020) . (25) Lorenz, K., Schmitt, J. P., Schmitteckert, E. M. & Lohse, M. J. 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(27) Ounkomol, C., Seshamani, S., Maleckar, M. M., Collman, F. & Johnson, G. R. Label-free prediction of three-dimensional fluorescence images from transmitted-light microscopy. Nature Methods 15 (11), 917–920 (2018) . (28) Viana, M. P. et al. Integrated intracellular organization and its variations in human iPS cells. Nature 613 (7943), 345–354 (2023) . Grüneboom, A. et al. A network of trans-cortical capillaries as mainstay for blood circulation in long bones. Nature Metabolism 1 (2), 236–250 (2019) . (27) Ounkomol, C., Seshamani, S., Maleckar, M. M., Collman, F. & Johnson, G. R. Label-free prediction of three-dimensional fluorescence images from transmitted-light microscopy. Nature Methods 15 (11), 917–920 (2018) . (28) Viana, M. P. et al. Integrated intracellular organization and its variations in human iPS cells. Nature 613 (7943), 345–354 (2023) . Ounkomol, C., Seshamani, S., Maleckar, M. M., Collman, F. & Johnson, G. R. 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A new type of ERK1/2 autophosphorylation causes cardiac hypertrophy. Nature Medicine 15 (1), 75–83 (2009) . (26) Grüneboom, A. et al. A network of trans-cortical capillaries as mainstay for blood circulation in long bones. Nature Metabolism 1 (2), 236–250 (2019) . (27) Ounkomol, C., Seshamani, S., Maleckar, M. M., Collman, F. & Johnson, G. R. Label-free prediction of three-dimensional fluorescence images from transmitted-light microscopy. Nature Methods 15 (11), 917–920 (2018) . (28) Viana, M. P. et al. Integrated intracellular organization and its variations in human iPS cells. Nature 613 (7943), 345–354 (2023) . Mahecic, D. et al. Event-driven acquisition for content-enriched microscopy. Nature Methods 19 (10), 1262–1267 (2022) . (9) Desislavov, R., Martínez-Plumed, F. & Hernández-Orallo, J. Trends in AI inference energy consumption: Beyond the performance-vs-parameter laws of deep learning. Sustainable Computing: Informatics and Systems 38, 100857 (2023) . (10) Xu, X. et al. 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URL https://github.com/microsoft/nni. (17) AskariHemmat, M. et al. U-Net Fixed-Point Quantization for Medical Image Segmentation. Large-Scale Annotation of Biomedical Data and Expert Label Synthesis and Hardware Aware Learning for Medical Imaging and Computer Assisted Intervention 115–124 (2019) . (18) Micikevicius, P. et al. FP8 Formats for Deep Learning (2022). ArXiv:2209.05433 [cs]. (19) Han, S., Pool, J., Tran, J. & Dally, W. J. Learning both Weights and Connections for Efficient Neural Networks (2015). ArXiv:1506.02626 [cs] . (20) He, Y., Liu, P., Wang, Z., Hu, Z. & Yang, Y. Filter Pruning via Geometric Median for Deep Convolutional Neural Networks Acceleration (2018). ArXiv:1811.00250 [cs] . (21) Weigert, M. et al. Content-aware image restoration: pushing the limits of fluorescence microscopy. Nature Methods 15 (12), 1090–1097 (2018) . (22) Cardoso, M. J. et al. MONAI: An open-source framework for deep learning in healthcare (2022). ArXiv:2211.02701 [cs] . (23) Cardiff, R. D., Miller, C. H. & Munn, R. J. Manual Hematoxylin and Eosin Staining of Mouse Tissue Sections. Cold Spring Harbor Protocols 2014 (6), pdb.prot073411 (2014) . (24) Tomasovic, A. et al. Interference with ERK-dimerization at the nucleocytosolic interface targets pathological ERK1/2 signaling without cardiotoxic side-effects. Nature Communications 11 (1), 1733 (2020) . (25) Lorenz, K., Schmitt, J. P., Schmitteckert, E. M. & Lohse, M. J. A new type of ERK1/2 autophosphorylation causes cardiac hypertrophy. Nature Medicine 15 (1), 75–83 (2009) . (26) Grüneboom, A. et al. A network of trans-cortical capillaries as mainstay for blood circulation in long bones. Nature Metabolism 1 (2), 236–250 (2019) . (27) Ounkomol, C., Seshamani, S., Maleckar, M. M., Collman, F. & Johnson, G. R. Label-free prediction of three-dimensional fluorescence images from transmitted-light microscopy. Nature Methods 15 (11), 917–920 (2018) . (28) Viana, M. P. et al. Integrated intracellular organization and its variations in human iPS cells. Nature 613 (7943), 345–354 (2023) . Desislavov, R., Martínez-Plumed, F. & Hernández-Orallo, J. Trends in AI inference energy consumption: Beyond the performance-vs-parameter laws of deep learning. Sustainable Computing: Informatics and Systems 38, 100857 (2023) . (10) Xu, X. et al. Quantization of Fully Convolutional Networks for Accurate Biomedical Image Segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2018) . (11) Gou, J., Yu, B., Maybank, S. J. & Tao, D. Knowledge Distillation: A Survey. International Journal of Computer Vision 129 (6), 1789–1819 (2021) . (12) Ding, C. et al. REQ-YOLO: A Resource-Aware, Efficient Quantization Framework for Object Detection on FPGAs (2019). ArXiv:1909.13396 [cs]. (13) Kozlov, A., Lazarevich, I., Shamporov, V., Lyalyushkin, N. & Gorbachev, Y. Neural Network Compression Framework for fast model inference (2020). ArXiv:2002.08679 [cs, eess]. (14) Li, Y. et al. MQBench: Towards Reproducible and Deployable Model Quantization Benchmark. Neural Information Processing Systems (2021) . (15) Siddegowda, S. et al. Neural Network Quantization with AI Model Efficiency Toolkit (AIMET) (2022). ArXiv:2201.08442 [cs.LG]. (16) Microsoft. Neural Network Intelligence (2022). URL https://github.com/microsoft/nni. (17) AskariHemmat, M. et al. U-Net Fixed-Point Quantization for Medical Image Segmentation. Large-Scale Annotation of Biomedical Data and Expert Label Synthesis and Hardware Aware Learning for Medical Imaging and Computer Assisted Intervention 115–124 (2019) . (18) Micikevicius, P. et al. FP8 Formats for Deep Learning (2022). ArXiv:2209.05433 [cs]. (19) Han, S., Pool, J., Tran, J. & Dally, W. J. Learning both Weights and Connections for Efficient Neural Networks (2015). ArXiv:1506.02626 [cs] . (20) He, Y., Liu, P., Wang, Z., Hu, Z. & Yang, Y. Filter Pruning via Geometric Median for Deep Convolutional Neural Networks Acceleration (2018). ArXiv:1811.00250 [cs] . (21) Weigert, M. et al. Content-aware image restoration: pushing the limits of fluorescence microscopy. Nature Methods 15 (12), 1090–1097 (2018) . (22) Cardoso, M. J. et al. MONAI: An open-source framework for deep learning in healthcare (2022). ArXiv:2211.02701 [cs] . (23) Cardiff, R. D., Miller, C. H. & Munn, R. J. Manual Hematoxylin and Eosin Staining of Mouse Tissue Sections. Cold Spring Harbor Protocols 2014 (6), pdb.prot073411 (2014) . (24) Tomasovic, A. et al. Interference with ERK-dimerization at the nucleocytosolic interface targets pathological ERK1/2 signaling without cardiotoxic side-effects. Nature Communications 11 (1), 1733 (2020) . (25) Lorenz, K., Schmitt, J. P., Schmitteckert, E. M. & Lohse, M. J. A new type of ERK1/2 autophosphorylation causes cardiac hypertrophy. Nature Medicine 15 (1), 75–83 (2009) . (26) Grüneboom, A. et al. A network of trans-cortical capillaries as mainstay for blood circulation in long bones. Nature Metabolism 1 (2), 236–250 (2019) . (27) Ounkomol, C., Seshamani, S., Maleckar, M. M., Collman, F. & Johnson, G. R. Label-free prediction of three-dimensional fluorescence images from transmitted-light microscopy. Nature Methods 15 (11), 917–920 (2018) . (28) Viana, M. P. et al. Integrated intracellular organization and its variations in human iPS cells. Nature 613 (7943), 345–354 (2023) . Xu, X. et al. Quantization of Fully Convolutional Networks for Accurate Biomedical Image Segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2018) . (11) Gou, J., Yu, B., Maybank, S. J. & Tao, D. Knowledge Distillation: A Survey. International Journal of Computer Vision 129 (6), 1789–1819 (2021) . (12) Ding, C. et al. REQ-YOLO: A Resource-Aware, Efficient Quantization Framework for Object Detection on FPGAs (2019). ArXiv:1909.13396 [cs]. (13) Kozlov, A., Lazarevich, I., Shamporov, V., Lyalyushkin, N. & Gorbachev, Y. Neural Network Compression Framework for fast model inference (2020). ArXiv:2002.08679 [cs, eess]. (14) Li, Y. et al. MQBench: Towards Reproducible and Deployable Model Quantization Benchmark. Neural Information Processing Systems (2021) . (15) Siddegowda, S. et al. Neural Network Quantization with AI Model Efficiency Toolkit (AIMET) (2022). ArXiv:2201.08442 [cs.LG]. (16) Microsoft. Neural Network Intelligence (2022). URL https://github.com/microsoft/nni. (17) AskariHemmat, M. et al. U-Net Fixed-Point Quantization for Medical Image Segmentation. Large-Scale Annotation of Biomedical Data and Expert Label Synthesis and Hardware Aware Learning for Medical Imaging and Computer Assisted Intervention 115–124 (2019) . (18) Micikevicius, P. et al. FP8 Formats for Deep Learning (2022). ArXiv:2209.05433 [cs]. (19) Han, S., Pool, J., Tran, J. & Dally, W. J. Learning both Weights and Connections for Efficient Neural Networks (2015). ArXiv:1506.02626 [cs] . (20) He, Y., Liu, P., Wang, Z., Hu, Z. & Yang, Y. Filter Pruning via Geometric Median for Deep Convolutional Neural Networks Acceleration (2018). ArXiv:1811.00250 [cs] . (21) Weigert, M. et al. Content-aware image restoration: pushing the limits of fluorescence microscopy. Nature Methods 15 (12), 1090–1097 (2018) . (22) Cardoso, M. J. et al. MONAI: An open-source framework for deep learning in healthcare (2022). ArXiv:2211.02701 [cs] . (23) Cardiff, R. D., Miller, C. H. & Munn, R. J. Manual Hematoxylin and Eosin Staining of Mouse Tissue Sections. Cold Spring Harbor Protocols 2014 (6), pdb.prot073411 (2014) . (24) Tomasovic, A. et al. Interference with ERK-dimerization at the nucleocytosolic interface targets pathological ERK1/2 signaling without cardiotoxic side-effects. Nature Communications 11 (1), 1733 (2020) . (25) Lorenz, K., Schmitt, J. P., Schmitteckert, E. M. & Lohse, M. J. A new type of ERK1/2 autophosphorylation causes cardiac hypertrophy. Nature Medicine 15 (1), 75–83 (2009) . (26) Grüneboom, A. et al. A network of trans-cortical capillaries as mainstay for blood circulation in long bones. Nature Metabolism 1 (2), 236–250 (2019) . (27) Ounkomol, C., Seshamani, S., Maleckar, M. M., Collman, F. & Johnson, G. R. Label-free prediction of three-dimensional fluorescence images from transmitted-light microscopy. Nature Methods 15 (11), 917–920 (2018) . (28) Viana, M. P. et al. Integrated intracellular organization and its variations in human iPS cells. Nature 613 (7943), 345–354 (2023) . Gou, J., Yu, B., Maybank, S. J. & Tao, D. Knowledge Distillation: A Survey. International Journal of Computer Vision 129 (6), 1789–1819 (2021) . (12) Ding, C. et al. REQ-YOLO: A Resource-Aware, Efficient Quantization Framework for Object Detection on FPGAs (2019). ArXiv:1909.13396 [cs]. (13) Kozlov, A., Lazarevich, I., Shamporov, V., Lyalyushkin, N. & Gorbachev, Y. Neural Network Compression Framework for fast model inference (2020). ArXiv:2002.08679 [cs, eess]. (14) Li, Y. et al. MQBench: Towards Reproducible and Deployable Model Quantization Benchmark. Neural Information Processing Systems (2021) . (15) Siddegowda, S. et al. Neural Network Quantization with AI Model Efficiency Toolkit (AIMET) (2022). ArXiv:2201.08442 [cs.LG]. (16) Microsoft. Neural Network Intelligence (2022). URL https://github.com/microsoft/nni. (17) AskariHemmat, M. et al. U-Net Fixed-Point Quantization for Medical Image Segmentation. Large-Scale Annotation of Biomedical Data and Expert Label Synthesis and Hardware Aware Learning for Medical Imaging and Computer Assisted Intervention 115–124 (2019) . (18) Micikevicius, P. et al. FP8 Formats for Deep Learning (2022). ArXiv:2209.05433 [cs]. (19) Han, S., Pool, J., Tran, J. & Dally, W. J. Learning both Weights and Connections for Efficient Neural Networks (2015). ArXiv:1506.02626 [cs] . (20) He, Y., Liu, P., Wang, Z., Hu, Z. & Yang, Y. Filter Pruning via Geometric Median for Deep Convolutional Neural Networks Acceleration (2018). ArXiv:1811.00250 [cs] . (21) Weigert, M. et al. Content-aware image restoration: pushing the limits of fluorescence microscopy. Nature Methods 15 (12), 1090–1097 (2018) . (22) Cardoso, M. J. et al. MONAI: An open-source framework for deep learning in healthcare (2022). ArXiv:2211.02701 [cs] . (23) Cardiff, R. D., Miller, C. H. & Munn, R. J. Manual Hematoxylin and Eosin Staining of Mouse Tissue Sections. Cold Spring Harbor Protocols 2014 (6), pdb.prot073411 (2014) . (24) Tomasovic, A. et al. Interference with ERK-dimerization at the nucleocytosolic interface targets pathological ERK1/2 signaling without cardiotoxic side-effects. Nature Communications 11 (1), 1733 (2020) . (25) Lorenz, K., Schmitt, J. P., Schmitteckert, E. M. & Lohse, M. J. A new type of ERK1/2 autophosphorylation causes cardiac hypertrophy. Nature Medicine 15 (1), 75–83 (2009) . (26) Grüneboom, A. et al. A network of trans-cortical capillaries as mainstay for blood circulation in long bones. Nature Metabolism 1 (2), 236–250 (2019) . (27) Ounkomol, C., Seshamani, S., Maleckar, M. M., Collman, F. & Johnson, G. R. Label-free prediction of three-dimensional fluorescence images from transmitted-light microscopy. Nature Methods 15 (11), 917–920 (2018) . (28) Viana, M. P. et al. Integrated intracellular organization and its variations in human iPS cells. Nature 613 (7943), 345–354 (2023) . Ding, C. et al. REQ-YOLO: A Resource-Aware, Efficient Quantization Framework for Object Detection on FPGAs (2019). ArXiv:1909.13396 [cs]. (13) Kozlov, A., Lazarevich, I., Shamporov, V., Lyalyushkin, N. & Gorbachev, Y. Neural Network Compression Framework for fast model inference (2020). ArXiv:2002.08679 [cs, eess]. (14) Li, Y. et al. MQBench: Towards Reproducible and Deployable Model Quantization Benchmark. Neural Information Processing Systems (2021) . (15) Siddegowda, S. et al. Neural Network Quantization with AI Model Efficiency Toolkit (AIMET) (2022). ArXiv:2201.08442 [cs.LG]. (16) Microsoft. Neural Network Intelligence (2022). URL https://github.com/microsoft/nni. (17) AskariHemmat, M. et al. U-Net Fixed-Point Quantization for Medical Image Segmentation. Large-Scale Annotation of Biomedical Data and Expert Label Synthesis and Hardware Aware Learning for Medical Imaging and Computer Assisted Intervention 115–124 (2019) . (18) Micikevicius, P. et al. FP8 Formats for Deep Learning (2022). ArXiv:2209.05433 [cs]. (19) Han, S., Pool, J., Tran, J. & Dally, W. J. Learning both Weights and Connections for Efficient Neural Networks (2015). ArXiv:1506.02626 [cs] . (20) He, Y., Liu, P., Wang, Z., Hu, Z. & Yang, Y. Filter Pruning via Geometric Median for Deep Convolutional Neural Networks Acceleration (2018). ArXiv:1811.00250 [cs] . (21) Weigert, M. et al. 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Nature Medicine 15 (1), 75–83 (2009) . (26) Grüneboom, A. et al. A network of trans-cortical capillaries as mainstay for blood circulation in long bones. Nature Metabolism 1 (2), 236–250 (2019) . (27) Ounkomol, C., Seshamani, S., Maleckar, M. M., Collman, F. & Johnson, G. R. Label-free prediction of three-dimensional fluorescence images from transmitted-light microscopy. Nature Methods 15 (11), 917–920 (2018) . (28) Viana, M. P. et al. Integrated intracellular organization and its variations in human iPS cells. Nature 613 (7943), 345–354 (2023) . Desislavov, R., Martínez-Plumed, F. & Hernández-Orallo, J. Trends in AI inference energy consumption: Beyond the performance-vs-parameter laws of deep learning. Sustainable Computing: Informatics and Systems 38, 100857 (2023) . (10) Xu, X. et al. Quantization of Fully Convolutional Networks for Accurate Biomedical Image Segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2018) . (11) Gou, J., Yu, B., Maybank, S. J. & Tao, D. Knowledge Distillation: A Survey. International Journal of Computer Vision 129 (6), 1789–1819 (2021) . (12) Ding, C. et al. REQ-YOLO: A Resource-Aware, Efficient Quantization Framework for Object Detection on FPGAs (2019). ArXiv:1909.13396 [cs]. (13) Kozlov, A., Lazarevich, I., Shamporov, V., Lyalyushkin, N. & Gorbachev, Y. Neural Network Compression Framework for fast model inference (2020). ArXiv:2002.08679 [cs, eess]. (14) Li, Y. et al. MQBench: Towards Reproducible and Deployable Model Quantization Benchmark. Neural Information Processing Systems (2021) . (15) Siddegowda, S. et al. Neural Network Quantization with AI Model Efficiency Toolkit (AIMET) (2022). ArXiv:2201.08442 [cs.LG]. (16) Microsoft. Neural Network Intelligence (2022). URL https://github.com/microsoft/nni. (17) AskariHemmat, M. et al. U-Net Fixed-Point Quantization for Medical Image Segmentation. 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Quantization of Fully Convolutional Networks for Accurate Biomedical Image Segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2018) . (11) Gou, J., Yu, B., Maybank, S. J. & Tao, D. Knowledge Distillation: A Survey. International Journal of Computer Vision 129 (6), 1789–1819 (2021) . (12) Ding, C. et al. REQ-YOLO: A Resource-Aware, Efficient Quantization Framework for Object Detection on FPGAs (2019). ArXiv:1909.13396 [cs]. (13) Kozlov, A., Lazarevich, I., Shamporov, V., Lyalyushkin, N. & Gorbachev, Y. Neural Network Compression Framework for fast model inference (2020). ArXiv:2002.08679 [cs, eess]. (14) Li, Y. et al. MQBench: Towards Reproducible and Deployable Model Quantization Benchmark. Neural Information Processing Systems (2021) . (15) Siddegowda, S. et al. Neural Network Quantization with AI Model Efficiency Toolkit (AIMET) (2022). ArXiv:2201.08442 [cs.LG]. (16) Microsoft. Neural Network Intelligence (2022). URL https://github.com/microsoft/nni. (17) AskariHemmat, M. et al. U-Net Fixed-Point Quantization for Medical Image Segmentation. Large-Scale Annotation of Biomedical Data and Expert Label Synthesis and Hardware Aware Learning for Medical Imaging and Computer Assisted Intervention 115–124 (2019) . (18) Micikevicius, P. et al. FP8 Formats for Deep Learning (2022). ArXiv:2209.05433 [cs]. (19) Han, S., Pool, J., Tran, J. & Dally, W. J. Learning both Weights and Connections for Efficient Neural Networks (2015). ArXiv:1506.02626 [cs] . (20) He, Y., Liu, P., Wang, Z., Hu, Z. & Yang, Y. Filter Pruning via Geometric Median for Deep Convolutional Neural Networks Acceleration (2018). ArXiv:1811.00250 [cs] . (21) Weigert, M. et al. Content-aware image restoration: pushing the limits of fluorescence microscopy. Nature Methods 15 (12), 1090–1097 (2018) . (22) Cardoso, M. J. et al. MONAI: An open-source framework for deep learning in healthcare (2022). ArXiv:2211.02701 [cs] . (23) Cardiff, R. D., Miller, C. H. & Munn, R. J. Manual Hematoxylin and Eosin Staining of Mouse Tissue Sections. Cold Spring Harbor Protocols 2014 (6), pdb.prot073411 (2014) . (24) Tomasovic, A. et al. Interference with ERK-dimerization at the nucleocytosolic interface targets pathological ERK1/2 signaling without cardiotoxic side-effects. Nature Communications 11 (1), 1733 (2020) . (25) Lorenz, K., Schmitt, J. P., Schmitteckert, E. M. & Lohse, M. J. A new type of ERK1/2 autophosphorylation causes cardiac hypertrophy. Nature Medicine 15 (1), 75–83 (2009) . (26) Grüneboom, A. et al. A network of trans-cortical capillaries as mainstay for blood circulation in long bones. Nature Metabolism 1 (2), 236–250 (2019) . (27) Ounkomol, C., Seshamani, S., Maleckar, M. M., Collman, F. & Johnson, G. R. Label-free prediction of three-dimensional fluorescence images from transmitted-light microscopy. Nature Methods 15 (11), 917–920 (2018) . (28) Viana, M. P. et al. Integrated intracellular organization and its variations in human iPS cells. Nature 613 (7943), 345–354 (2023) . Gou, J., Yu, B., Maybank, S. J. & Tao, D. Knowledge Distillation: A Survey. International Journal of Computer Vision 129 (6), 1789–1819 (2021) . (12) Ding, C. et al. REQ-YOLO: A Resource-Aware, Efficient Quantization Framework for Object Detection on FPGAs (2019). ArXiv:1909.13396 [cs]. (13) Kozlov, A., Lazarevich, I., Shamporov, V., Lyalyushkin, N. & Gorbachev, Y. Neural Network Compression Framework for fast model inference (2020). ArXiv:2002.08679 [cs, eess]. (14) Li, Y. et al. MQBench: Towards Reproducible and Deployable Model Quantization Benchmark. Neural Information Processing Systems (2021) . (15) Siddegowda, S. et al. Neural Network Quantization with AI Model Efficiency Toolkit (AIMET) (2022). ArXiv:2201.08442 [cs.LG]. (16) Microsoft. Neural Network Intelligence (2022). URL https://github.com/microsoft/nni. (17) AskariHemmat, M. et al. 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Manual Hematoxylin and Eosin Staining of Mouse Tissue Sections. Cold Spring Harbor Protocols 2014 (6), pdb.prot073411 (2014) . (24) Tomasovic, A. et al. Interference with ERK-dimerization at the nucleocytosolic interface targets pathological ERK1/2 signaling without cardiotoxic side-effects. Nature Communications 11 (1), 1733 (2020) . (25) Lorenz, K., Schmitt, J. P., Schmitteckert, E. M. & Lohse, M. J. A new type of ERK1/2 autophosphorylation causes cardiac hypertrophy. Nature Medicine 15 (1), 75–83 (2009) . (26) Grüneboom, A. et al. A network of trans-cortical capillaries as mainstay for blood circulation in long bones. Nature Metabolism 1 (2), 236–250 (2019) . (27) Ounkomol, C., Seshamani, S., Maleckar, M. M., Collman, F. & Johnson, G. R. Label-free prediction of three-dimensional fluorescence images from transmitted-light microscopy. Nature Methods 15 (11), 917–920 (2018) . (28) Viana, M. P. et al. Integrated intracellular organization and its variations in human iPS cells. Nature 613 (7943), 345–354 (2023) . Ding, C. et al. REQ-YOLO: A Resource-Aware, Efficient Quantization Framework for Object Detection on FPGAs (2019). ArXiv:1909.13396 [cs]. (13) Kozlov, A., Lazarevich, I., Shamporov, V., Lyalyushkin, N. & Gorbachev, Y. Neural Network Compression Framework for fast model inference (2020). ArXiv:2002.08679 [cs, eess]. (14) Li, Y. et al. MQBench: Towards Reproducible and Deployable Model Quantization Benchmark. Neural Information Processing Systems (2021) . (15) Siddegowda, S. et al. Neural Network Quantization with AI Model Efficiency Toolkit (AIMET) (2022). ArXiv:2201.08442 [cs.LG]. (16) Microsoft. Neural Network Intelligence (2022). URL https://github.com/microsoft/nni. (17) AskariHemmat, M. et al. U-Net Fixed-Point Quantization for Medical Image Segmentation. Large-Scale Annotation of Biomedical Data and Expert Label Synthesis and Hardware Aware Learning for Medical Imaging and Computer Assisted Intervention 115–124 (2019) . 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Neural Network Compression Framework for fast model inference (2020). ArXiv:2002.08679 [cs, eess]. (14) Li, Y. et al. MQBench: Towards Reproducible and Deployable Model Quantization Benchmark. Neural Information Processing Systems (2021) . (15) Siddegowda, S. et al. Neural Network Quantization with AI Model Efficiency Toolkit (AIMET) (2022). ArXiv:2201.08442 [cs.LG]. (16) Microsoft. Neural Network Intelligence (2022). URL https://github.com/microsoft/nni. (17) AskariHemmat, M. et al. U-Net Fixed-Point Quantization for Medical Image Segmentation. Large-Scale Annotation of Biomedical Data and Expert Label Synthesis and Hardware Aware Learning for Medical Imaging and Computer Assisted Intervention 115–124 (2019) . (18) Micikevicius, P. et al. FP8 Formats for Deep Learning (2022). ArXiv:2209.05433 [cs]. (19) Han, S., Pool, J., Tran, J. & Dally, W. J. Learning both Weights and Connections for Efficient Neural Networks (2015). ArXiv:1506.02626 [cs] . (20) He, Y., Liu, P., Wang, Z., Hu, Z. & Yang, Y. Filter Pruning via Geometric Median for Deep Convolutional Neural Networks Acceleration (2018). ArXiv:1811.00250 [cs] . (21) Weigert, M. et al. Content-aware image restoration: pushing the limits of fluorescence microscopy. Nature Methods 15 (12), 1090–1097 (2018) . (22) Cardoso, M. J. et al. MONAI: An open-source framework for deep learning in healthcare (2022). ArXiv:2211.02701 [cs] . (23) Cardiff, R. D., Miller, C. H. & Munn, R. J. Manual Hematoxylin and Eosin Staining of Mouse Tissue Sections. Cold Spring Harbor Protocols 2014 (6), pdb.prot073411 (2014) . (24) Tomasovic, A. et al. Interference with ERK-dimerization at the nucleocytosolic interface targets pathological ERK1/2 signaling without cardiotoxic side-effects. Nature Communications 11 (1), 1733 (2020) . (25) Lorenz, K., Schmitt, J. P., Schmitteckert, E. M. & Lohse, M. J. A new type of ERK1/2 autophosphorylation causes cardiac hypertrophy. Nature Medicine 15 (1), 75–83 (2009) . (26) Grüneboom, A. et al. A network of trans-cortical capillaries as mainstay for blood circulation in long bones. Nature Metabolism 1 (2), 236–250 (2019) . (27) Ounkomol, C., Seshamani, S., Maleckar, M. M., Collman, F. & Johnson, G. R. Label-free prediction of three-dimensional fluorescence images from transmitted-light microscopy. Nature Methods 15 (11), 917–920 (2018) . (28) Viana, M. P. et al. Integrated intracellular organization and its variations in human iPS cells. Nature 613 (7943), 345–354 (2023) . Li, Y. et al. MQBench: Towards Reproducible and Deployable Model Quantization Benchmark. Neural Information Processing Systems (2021) . (15) Siddegowda, S. et al. Neural Network Quantization with AI Model Efficiency Toolkit (AIMET) (2022). ArXiv:2201.08442 [cs.LG]. (16) Microsoft. Neural Network Intelligence (2022). URL https://github.com/microsoft/nni. (17) AskariHemmat, M. et al. U-Net Fixed-Point Quantization for Medical Image Segmentation. Large-Scale Annotation of Biomedical Data and Expert Label Synthesis and Hardware Aware Learning for Medical Imaging and Computer Assisted Intervention 115–124 (2019) . (18) Micikevicius, P. et al. FP8 Formats for Deep Learning (2022). ArXiv:2209.05433 [cs]. (19) Han, S., Pool, J., Tran, J. & Dally, W. J. Learning both Weights and Connections for Efficient Neural Networks (2015). ArXiv:1506.02626 [cs] . (20) He, Y., Liu, P., Wang, Z., Hu, Z. & Yang, Y. Filter Pruning via Geometric Median for Deep Convolutional Neural Networks Acceleration (2018). ArXiv:1811.00250 [cs] . (21) Weigert, M. et al. Content-aware image restoration: pushing the limits of fluorescence microscopy. Nature Methods 15 (12), 1090–1097 (2018) . (22) Cardoso, M. J. et al. MONAI: An open-source framework for deep learning in healthcare (2022). ArXiv:2211.02701 [cs] . (23) Cardiff, R. D., Miller, C. H. & Munn, R. J. Manual Hematoxylin and Eosin Staining of Mouse Tissue Sections. Cold Spring Harbor Protocols 2014 (6), pdb.prot073411 (2014) . (24) Tomasovic, A. et al. Interference with ERK-dimerization at the nucleocytosolic interface targets pathological ERK1/2 signaling without cardiotoxic side-effects. Nature Communications 11 (1), 1733 (2020) . (25) Lorenz, K., Schmitt, J. P., Schmitteckert, E. M. & Lohse, M. J. A new type of ERK1/2 autophosphorylation causes cardiac hypertrophy. Nature Medicine 15 (1), 75–83 (2009) . (26) Grüneboom, A. et al. A network of trans-cortical capillaries as mainstay for blood circulation in long bones. Nature Metabolism 1 (2), 236–250 (2019) . (27) Ounkomol, C., Seshamani, S., Maleckar, M. M., Collman, F. & Johnson, G. R. Label-free prediction of three-dimensional fluorescence images from transmitted-light microscopy. Nature Methods 15 (11), 917–920 (2018) . (28) Viana, M. P. et al. Integrated intracellular organization and its variations in human iPS cells. Nature 613 (7943), 345–354 (2023) . Siddegowda, S. et al. Neural Network Quantization with AI Model Efficiency Toolkit (AIMET) (2022). ArXiv:2201.08442 [cs.LG]. (16) Microsoft. Neural Network Intelligence (2022). URL https://github.com/microsoft/nni. (17) AskariHemmat, M. et al. U-Net Fixed-Point Quantization for Medical Image Segmentation. Large-Scale Annotation of Biomedical Data and Expert Label Synthesis and Hardware Aware Learning for Medical Imaging and Computer Assisted Intervention 115–124 (2019) . (18) Micikevicius, P. et al. FP8 Formats for Deep Learning (2022). ArXiv:2209.05433 [cs]. (19) Han, S., Pool, J., Tran, J. & Dally, W. J. Learning both Weights and Connections for Efficient Neural Networks (2015). ArXiv:1506.02626 [cs] . (20) He, Y., Liu, P., Wang, Z., Hu, Z. & Yang, Y. Filter Pruning via Geometric Median for Deep Convolutional Neural Networks Acceleration (2018). ArXiv:1811.00250 [cs] . (21) Weigert, M. et al. Content-aware image restoration: pushing the limits of fluorescence microscopy. Nature Methods 15 (12), 1090–1097 (2018) . (22) Cardoso, M. J. et al. MONAI: An open-source framework for deep learning in healthcare (2022). ArXiv:2211.02701 [cs] . (23) Cardiff, R. D., Miller, C. H. & Munn, R. J. Manual Hematoxylin and Eosin Staining of Mouse Tissue Sections. Cold Spring Harbor Protocols 2014 (6), pdb.prot073411 (2014) . (24) Tomasovic, A. et al. Interference with ERK-dimerization at the nucleocytosolic interface targets pathological ERK1/2 signaling without cardiotoxic side-effects. Nature Communications 11 (1), 1733 (2020) . (25) Lorenz, K., Schmitt, J. P., Schmitteckert, E. M. & Lohse, M. J. A new type of ERK1/2 autophosphorylation causes cardiac hypertrophy. Nature Medicine 15 (1), 75–83 (2009) . (26) Grüneboom, A. et al. A network of trans-cortical capillaries as mainstay for blood circulation in long bones. Nature Metabolism 1 (2), 236–250 (2019) . (27) Ounkomol, C., Seshamani, S., Maleckar, M. M., Collman, F. & Johnson, G. R. Label-free prediction of three-dimensional fluorescence images from transmitted-light microscopy. Nature Methods 15 (11), 917–920 (2018) . (28) Viana, M. P. et al. Integrated intracellular organization and its variations in human iPS cells. Nature 613 (7943), 345–354 (2023) . Microsoft. Neural Network Intelligence (2022). URL https://github.com/microsoft/nni. (17) AskariHemmat, M. et al. U-Net Fixed-Point Quantization for Medical Image Segmentation. Large-Scale Annotation of Biomedical Data and Expert Label Synthesis and Hardware Aware Learning for Medical Imaging and Computer Assisted Intervention 115–124 (2019) . (18) Micikevicius, P. et al. FP8 Formats for Deep Learning (2022). ArXiv:2209.05433 [cs]. (19) Han, S., Pool, J., Tran, J. & Dally, W. J. Learning both Weights and Connections for Efficient Neural Networks (2015). ArXiv:1506.02626 [cs] . (20) He, Y., Liu, P., Wang, Z., Hu, Z. & Yang, Y. 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A network of trans-cortical capillaries as mainstay for blood circulation in long bones. Nature Metabolism 1 (2), 236–250 (2019) . (27) Ounkomol, C., Seshamani, S., Maleckar, M. M., Collman, F. & Johnson, G. R. Label-free prediction of three-dimensional fluorescence images from transmitted-light microscopy. Nature Methods 15 (11), 917–920 (2018) . (28) Viana, M. P. et al. Integrated intracellular organization and its variations in human iPS cells. Nature 613 (7943), 345–354 (2023) . AskariHemmat, M. et al. U-Net Fixed-Point Quantization for Medical Image Segmentation. Large-Scale Annotation of Biomedical Data and Expert Label Synthesis and Hardware Aware Learning for Medical Imaging and Computer Assisted Intervention 115–124 (2019) . (18) Micikevicius, P. et al. FP8 Formats for Deep Learning (2022). ArXiv:2209.05433 [cs]. (19) Han, S., Pool, J., Tran, J. & Dally, W. J. Learning both Weights and Connections for Efficient Neural Networks (2015). ArXiv:1506.02626 [cs] . 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Nature Medicine 15 (1), 75–83 (2009) . (26) Grüneboom, A. et al. A network of trans-cortical capillaries as mainstay for blood circulation in long bones. Nature Metabolism 1 (2), 236–250 (2019) . (27) Ounkomol, C., Seshamani, S., Maleckar, M. M., Collman, F. & Johnson, G. R. Label-free prediction of three-dimensional fluorescence images from transmitted-light microscopy. Nature Methods 15 (11), 917–920 (2018) . (28) Viana, M. P. et al. Integrated intracellular organization and its variations in human iPS cells. Nature 613 (7943), 345–354 (2023) . Weigert, M. et al. Content-aware image restoration: pushing the limits of fluorescence microscopy. Nature Methods 15 (12), 1090–1097 (2018) . (22) Cardoso, M. J. et al. MONAI: An open-source framework for deep learning in healthcare (2022). ArXiv:2211.02701 [cs] . (23) Cardiff, R. D., Miller, C. H. & Munn, R. J. Manual Hematoxylin and Eosin Staining of Mouse Tissue Sections. Cold Spring Harbor Protocols 2014 (6), pdb.prot073411 (2014) . 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Nature Metabolism 1 (2), 236–250 (2019) . (27) Ounkomol, C., Seshamani, S., Maleckar, M. M., Collman, F. & Johnson, G. R. Label-free prediction of three-dimensional fluorescence images from transmitted-light microscopy. Nature Methods 15 (11), 917–920 (2018) . (28) Viana, M. P. et al. Integrated intracellular organization and its variations in human iPS cells. Nature 613 (7943), 345–354 (2023) . Ounkomol, C., Seshamani, S., Maleckar, M. M., Collman, F. & Johnson, G. R. Label-free prediction of three-dimensional fluorescence images from transmitted-light microscopy. Nature Methods 15 (11), 917–920 (2018) . (28) Viana, M. P. et al. Integrated intracellular organization and its variations in human iPS cells. Nature 613 (7943), 345–354 (2023) . Viana, M. P. et al. Integrated intracellular organization and its variations in human iPS cells. Nature 613 (7943), 345–354 (2023) .
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Label-free prediction of three-dimensional fluorescence images from transmitted-light microscopy. Nature Methods 15 (11), 917–920 (2018) . (28) Viana, M. P. et al. Integrated intracellular organization and its variations in human iPS cells. Nature 613 (7943), 345–354 (2023) . Xu, X. et al. Quantization of Fully Convolutional Networks for Accurate Biomedical Image Segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2018) . (11) Gou, J., Yu, B., Maybank, S. J. & Tao, D. Knowledge Distillation: A Survey. International Journal of Computer Vision 129 (6), 1789–1819 (2021) . (12) Ding, C. et al. REQ-YOLO: A Resource-Aware, Efficient Quantization Framework for Object Detection on FPGAs (2019). ArXiv:1909.13396 [cs]. (13) Kozlov, A., Lazarevich, I., Shamporov, V., Lyalyushkin, N. & Gorbachev, Y. Neural Network Compression Framework for fast model inference (2020). ArXiv:2002.08679 [cs, eess]. (14) Li, Y. et al. 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ArXiv:1811.00250 [cs] . (21) Weigert, M. et al. Content-aware image restoration: pushing the limits of fluorescence microscopy. Nature Methods 15 (12), 1090–1097 (2018) . (22) Cardoso, M. J. et al. MONAI: An open-source framework for deep learning in healthcare (2022). ArXiv:2211.02701 [cs] . (23) Cardiff, R. D., Miller, C. H. & Munn, R. J. Manual Hematoxylin and Eosin Staining of Mouse Tissue Sections. Cold Spring Harbor Protocols 2014 (6), pdb.prot073411 (2014) . (24) Tomasovic, A. et al. Interference with ERK-dimerization at the nucleocytosolic interface targets pathological ERK1/2 signaling without cardiotoxic side-effects. Nature Communications 11 (1), 1733 (2020) . (25) Lorenz, K., Schmitt, J. P., Schmitteckert, E. M. & Lohse, M. J. A new type of ERK1/2 autophosphorylation causes cardiac hypertrophy. Nature Medicine 15 (1), 75–83 (2009) . (26) Grüneboom, A. et al. A network of trans-cortical capillaries as mainstay for blood circulation in long bones. Nature Metabolism 1 (2), 236–250 (2019) . (27) Ounkomol, C., Seshamani, S., Maleckar, M. M., Collman, F. & Johnson, G. R. Label-free prediction of three-dimensional fluorescence images from transmitted-light microscopy. Nature Methods 15 (11), 917–920 (2018) . (28) Viana, M. P. et al. Integrated intracellular organization and its variations in human iPS cells. Nature 613 (7943), 345–354 (2023) . Gou, J., Yu, B., Maybank, S. J. & Tao, D. Knowledge Distillation: A Survey. International Journal of Computer Vision 129 (6), 1789–1819 (2021) . (12) Ding, C. et al. REQ-YOLO: A Resource-Aware, Efficient Quantization Framework for Object Detection on FPGAs (2019). ArXiv:1909.13396 [cs]. (13) Kozlov, A., Lazarevich, I., Shamporov, V., Lyalyushkin, N. & Gorbachev, Y. Neural Network Compression Framework for fast model inference (2020). ArXiv:2002.08679 [cs, eess]. (14) Li, Y. et al. MQBench: Towards Reproducible and Deployable Model Quantization Benchmark. Neural Information Processing Systems (2021) . (15) Siddegowda, S. et al. Neural Network Quantization with AI Model Efficiency Toolkit (AIMET) (2022). ArXiv:2201.08442 [cs.LG]. (16) Microsoft. Neural Network Intelligence (2022). URL https://github.com/microsoft/nni. (17) AskariHemmat, M. et al. U-Net Fixed-Point Quantization for Medical Image Segmentation. Large-Scale Annotation of Biomedical Data and Expert Label Synthesis and Hardware Aware Learning for Medical Imaging and Computer Assisted Intervention 115–124 (2019) . (18) Micikevicius, P. et al. FP8 Formats for Deep Learning (2022). ArXiv:2209.05433 [cs]. (19) Han, S., Pool, J., Tran, J. & Dally, W. J. Learning both Weights and Connections for Efficient Neural Networks (2015). ArXiv:1506.02626 [cs] . (20) He, Y., Liu, P., Wang, Z., Hu, Z. & Yang, Y. Filter Pruning via Geometric Median for Deep Convolutional Neural Networks Acceleration (2018). ArXiv:1811.00250 [cs] . (21) Weigert, M. et al. Content-aware image restoration: pushing the limits of fluorescence microscopy. Nature Methods 15 (12), 1090–1097 (2018) . (22) Cardoso, M. J. et al. MONAI: An open-source framework for deep learning in healthcare (2022). ArXiv:2211.02701 [cs] . (23) Cardiff, R. D., Miller, C. H. & Munn, R. J. Manual Hematoxylin and Eosin Staining of Mouse Tissue Sections. Cold Spring Harbor Protocols 2014 (6), pdb.prot073411 (2014) . (24) Tomasovic, A. et al. Interference with ERK-dimerization at the nucleocytosolic interface targets pathological ERK1/2 signaling without cardiotoxic side-effects. Nature Communications 11 (1), 1733 (2020) . (25) Lorenz, K., Schmitt, J. P., Schmitteckert, E. M. & Lohse, M. J. A new type of ERK1/2 autophosphorylation causes cardiac hypertrophy. Nature Medicine 15 (1), 75–83 (2009) . (26) Grüneboom, A. et al. A network of trans-cortical capillaries as mainstay for blood circulation in long bones. Nature Metabolism 1 (2), 236–250 (2019) . 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Nature Medicine 15 (1), 75–83 (2009) . (26) Grüneboom, A. et al. A network of trans-cortical capillaries as mainstay for blood circulation in long bones. Nature Metabolism 1 (2), 236–250 (2019) . (27) Ounkomol, C., Seshamani, S., Maleckar, M. M., Collman, F. & Johnson, G. R. Label-free prediction of three-dimensional fluorescence images from transmitted-light microscopy. Nature Methods 15 (11), 917–920 (2018) . (28) Viana, M. P. et al. Integrated intracellular organization and its variations in human iPS cells. Nature 613 (7943), 345–354 (2023) . Weigert, M. et al. Content-aware image restoration: pushing the limits of fluorescence microscopy. Nature Methods 15 (12), 1090–1097 (2018) . (22) Cardoso, M. J. et al. MONAI: An open-source framework for deep learning in healthcare (2022). ArXiv:2211.02701 [cs] . (23) Cardiff, R. D., Miller, C. H. & Munn, R. J. Manual Hematoxylin and Eosin Staining of Mouse Tissue Sections. Cold Spring Harbor Protocols 2014 (6), pdb.prot073411 (2014) . (24) Tomasovic, A. et al. Interference with ERK-dimerization at the nucleocytosolic interface targets pathological ERK1/2 signaling without cardiotoxic side-effects. Nature Communications 11 (1), 1733 (2020) . (25) Lorenz, K., Schmitt, J. P., Schmitteckert, E. M. & Lohse, M. J. A new type of ERK1/2 autophosphorylation causes cardiac hypertrophy. Nature Medicine 15 (1), 75–83 (2009) . (26) Grüneboom, A. et al. A network of trans-cortical capillaries as mainstay for blood circulation in long bones. Nature Metabolism 1 (2), 236–250 (2019) . (27) Ounkomol, C., Seshamani, S., Maleckar, M. M., Collman, F. & Johnson, G. R. Label-free prediction of three-dimensional fluorescence images from transmitted-light microscopy. Nature Methods 15 (11), 917–920 (2018) . (28) Viana, M. P. et al. Integrated intracellular organization and its variations in human iPS cells. Nature 613 (7943), 345–354 (2023) . Cardoso, M. J. et al. MONAI: An open-source framework for deep learning in healthcare (2022). 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Integrated intracellular organization and its variations in human iPS cells. Nature 613 (7943), 345–354 (2023) . Cardiff, R. D., Miller, C. H. & Munn, R. J. Manual Hematoxylin and Eosin Staining of Mouse Tissue Sections. Cold Spring Harbor Protocols 2014 (6), pdb.prot073411 (2014) . (24) Tomasovic, A. et al. Interference with ERK-dimerization at the nucleocytosolic interface targets pathological ERK1/2 signaling without cardiotoxic side-effects. Nature Communications 11 (1), 1733 (2020) . (25) Lorenz, K., Schmitt, J. P., Schmitteckert, E. M. & Lohse, M. J. A new type of ERK1/2 autophosphorylation causes cardiac hypertrophy. Nature Medicine 15 (1), 75–83 (2009) . (26) Grüneboom, A. et al. A network of trans-cortical capillaries as mainstay for blood circulation in long bones. Nature Metabolism 1 (2), 236–250 (2019) . (27) Ounkomol, C., Seshamani, S., Maleckar, M. M., Collman, F. & Johnson, G. R. Label-free prediction of three-dimensional fluorescence images from transmitted-light microscopy. Nature Methods 15 (11), 917–920 (2018) . (28) Viana, M. P. et al. Integrated intracellular organization and its variations in human iPS cells. Nature 613 (7943), 345–354 (2023) . Tomasovic, A. et al. Interference with ERK-dimerization at the nucleocytosolic interface targets pathological ERK1/2 signaling without cardiotoxic side-effects. Nature Communications 11 (1), 1733 (2020) . (25) Lorenz, K., Schmitt, J. P., Schmitteckert, E. M. & Lohse, M. J. A new type of ERK1/2 autophosphorylation causes cardiac hypertrophy. Nature Medicine 15 (1), 75–83 (2009) . (26) Grüneboom, A. et al. A network of trans-cortical capillaries as mainstay for blood circulation in long bones. Nature Metabolism 1 (2), 236–250 (2019) . (27) Ounkomol, C., Seshamani, S., Maleckar, M. M., Collman, F. & Johnson, G. R. Label-free prediction of three-dimensional fluorescence images from transmitted-light microscopy. 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(27) Ounkomol, C., Seshamani, S., Maleckar, M. M., Collman, F. & Johnson, G. R. Label-free prediction of three-dimensional fluorescence images from transmitted-light microscopy. Nature Methods 15 (11), 917–920 (2018) . (28) Viana, M. P. et al. Integrated intracellular organization and its variations in human iPS cells. Nature 613 (7943), 345–354 (2023) . Han, S., Pool, J., Tran, J. & Dally, W. J. Learning both Weights and Connections for Efficient Neural Networks (2015). ArXiv:1506.02626 [cs] . (20) He, Y., Liu, P., Wang, Z., Hu, Z. & Yang, Y. Filter Pruning via Geometric Median for Deep Convolutional Neural Networks Acceleration (2018). ArXiv:1811.00250 [cs] . (21) Weigert, M. et al. Content-aware image restoration: pushing the limits of fluorescence microscopy. Nature Methods 15 (12), 1090–1097 (2018) . (22) Cardoso, M. J. et al. MONAI: An open-source framework for deep learning in healthcare (2022). ArXiv:2211.02701 [cs] . (23) Cardiff, R. D., Miller, C. H. & Munn, R. J. 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(25) Lorenz, K., Schmitt, J. P., Schmitteckert, E. M. & Lohse, M. J. A new type of ERK1/2 autophosphorylation causes cardiac hypertrophy. Nature Medicine 15 (1), 75–83 (2009) . (26) Grüneboom, A. et al. A network of trans-cortical capillaries as mainstay for blood circulation in long bones. Nature Metabolism 1 (2), 236–250 (2019) . (27) Ounkomol, C., Seshamani, S., Maleckar, M. M., Collman, F. & Johnson, G. R. Label-free prediction of three-dimensional fluorescence images from transmitted-light microscopy. Nature Methods 15 (11), 917–920 (2018) . (28) Viana, M. P. et al. Integrated intracellular organization and its variations in human iPS cells. Nature 613 (7943), 345–354 (2023) . Kozlov, A., Lazarevich, I., Shamporov, V., Lyalyushkin, N. & Gorbachev, Y. Neural Network Compression Framework for fast model inference (2020). ArXiv:2002.08679 [cs, eess]. (14) Li, Y. et al. MQBench: Towards Reproducible and Deployable Model Quantization Benchmark. Neural Information Processing Systems (2021) . (15) Siddegowda, S. et al. Neural Network Quantization with AI Model Efficiency Toolkit (AIMET) (2022). ArXiv:2201.08442 [cs.LG]. (16) Microsoft. Neural Network Intelligence (2022). URL https://github.com/microsoft/nni. (17) AskariHemmat, M. et al. U-Net Fixed-Point Quantization for Medical Image Segmentation. Large-Scale Annotation of Biomedical Data and Expert Label Synthesis and Hardware Aware Learning for Medical Imaging and Computer Assisted Intervention 115–124 (2019) . (18) Micikevicius, P. et al. FP8 Formats for Deep Learning (2022). ArXiv:2209.05433 [cs]. (19) Han, S., Pool, J., Tran, J. & Dally, W. J. Learning both Weights and Connections for Efficient Neural Networks (2015). ArXiv:1506.02626 [cs] . (20) He, Y., Liu, P., Wang, Z., Hu, Z. & Yang, Y. Filter Pruning via Geometric Median for Deep Convolutional Neural Networks Acceleration (2018). ArXiv:1811.00250 [cs] . (21) Weigert, M. et al. Content-aware image restoration: pushing the limits of fluorescence microscopy. Nature Methods 15 (12), 1090–1097 (2018) . (22) Cardoso, M. J. et al. MONAI: An open-source framework for deep learning in healthcare (2022). ArXiv:2211.02701 [cs] . (23) Cardiff, R. D., Miller, C. H. & Munn, R. J. Manual Hematoxylin and Eosin Staining of Mouse Tissue Sections. Cold Spring Harbor Protocols 2014 (6), pdb.prot073411 (2014) . (24) Tomasovic, A. et al. Interference with ERK-dimerization at the nucleocytosolic interface targets pathological ERK1/2 signaling without cardiotoxic side-effects. Nature Communications 11 (1), 1733 (2020) . (25) Lorenz, K., Schmitt, J. P., Schmitteckert, E. M. & Lohse, M. J. A new type of ERK1/2 autophosphorylation causes cardiac hypertrophy. Nature Medicine 15 (1), 75–83 (2009) . (26) Grüneboom, A. et al. A network of trans-cortical capillaries as mainstay for blood circulation in long bones. Nature Metabolism 1 (2), 236–250 (2019) . (27) Ounkomol, C., Seshamani, S., Maleckar, M. M., Collman, F. & Johnson, G. R. Label-free prediction of three-dimensional fluorescence images from transmitted-light microscopy. Nature Methods 15 (11), 917–920 (2018) . (28) Viana, M. P. et al. Integrated intracellular organization and its variations in human iPS cells. Nature 613 (7943), 345–354 (2023) . Li, Y. et al. MQBench: Towards Reproducible and Deployable Model Quantization Benchmark. Neural Information Processing Systems (2021) . (15) Siddegowda, S. et al. Neural Network Quantization with AI Model Efficiency Toolkit (AIMET) (2022). ArXiv:2201.08442 [cs.LG]. (16) Microsoft. Neural Network Intelligence (2022). URL https://github.com/microsoft/nni. (17) AskariHemmat, M. et al. U-Net Fixed-Point Quantization for Medical Image Segmentation. Large-Scale Annotation of Biomedical Data and Expert Label Synthesis and Hardware Aware Learning for Medical Imaging and Computer Assisted Intervention 115–124 (2019) . 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(16) Microsoft. Neural Network Intelligence (2022). URL https://github.com/microsoft/nni. (17) AskariHemmat, M. et al. U-Net Fixed-Point Quantization for Medical Image Segmentation. Large-Scale Annotation of Biomedical Data and Expert Label Synthesis and Hardware Aware Learning for Medical Imaging and Computer Assisted Intervention 115–124 (2019) . (18) Micikevicius, P. et al. FP8 Formats for Deep Learning (2022). ArXiv:2209.05433 [cs]. (19) Han, S., Pool, J., Tran, J. & Dally, W. J. Learning both Weights and Connections for Efficient Neural Networks (2015). ArXiv:1506.02626 [cs] . (20) He, Y., Liu, P., Wang, Z., Hu, Z. & Yang, Y. Filter Pruning via Geometric Median for Deep Convolutional Neural Networks Acceleration (2018). ArXiv:1811.00250 [cs] . (21) Weigert, M. et al. Content-aware image restoration: pushing the limits of fluorescence microscopy. Nature Methods 15 (12), 1090–1097 (2018) . (22) Cardoso, M. J. et al. MONAI: An open-source framework for deep learning in healthcare (2022). ArXiv:2211.02701 [cs] . (23) Cardiff, R. D., Miller, C. H. & Munn, R. J. Manual Hematoxylin and Eosin Staining of Mouse Tissue Sections. Cold Spring Harbor Protocols 2014 (6), pdb.prot073411 (2014) . (24) Tomasovic, A. et al. Interference with ERK-dimerization at the nucleocytosolic interface targets pathological ERK1/2 signaling without cardiotoxic side-effects. Nature Communications 11 (1), 1733 (2020) . (25) Lorenz, K., Schmitt, J. P., Schmitteckert, E. M. & Lohse, M. J. A new type of ERK1/2 autophosphorylation causes cardiac hypertrophy. Nature Medicine 15 (1), 75–83 (2009) . (26) Grüneboom, A. et al. A network of trans-cortical capillaries as mainstay for blood circulation in long bones. Nature Metabolism 1 (2), 236–250 (2019) . (27) Ounkomol, C., Seshamani, S., Maleckar, M. M., Collman, F. & Johnson, G. R. Label-free prediction of three-dimensional fluorescence images from transmitted-light microscopy. 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(27) Ounkomol, C., Seshamani, S., Maleckar, M. M., Collman, F. & Johnson, G. R. Label-free prediction of three-dimensional fluorescence images from transmitted-light microscopy. Nature Methods 15 (11), 917–920 (2018) . (28) Viana, M. P. et al. Integrated intracellular organization and its variations in human iPS cells. Nature 613 (7943), 345–354 (2023) . AskariHemmat, M. et al. U-Net Fixed-Point Quantization for Medical Image Segmentation. Large-Scale Annotation of Biomedical Data and Expert Label Synthesis and Hardware Aware Learning for Medical Imaging and Computer Assisted Intervention 115–124 (2019) . (18) Micikevicius, P. et al. FP8 Formats for Deep Learning (2022). ArXiv:2209.05433 [cs]. (19) Han, S., Pool, J., Tran, J. & Dally, W. J. Learning both Weights and Connections for Efficient Neural Networks (2015). ArXiv:1506.02626 [cs] . (20) He, Y., Liu, P., Wang, Z., Hu, Z. & Yang, Y. Filter Pruning via Geometric Median for Deep Convolutional Neural Networks Acceleration (2018). 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Nature Metabolism 1 (2), 236–250 (2019) . (27) Ounkomol, C., Seshamani, S., Maleckar, M. M., Collman, F. & Johnson, G. R. Label-free prediction of three-dimensional fluorescence images from transmitted-light microscopy. Nature Methods 15 (11), 917–920 (2018) . (28) Viana, M. P. et al. Integrated intracellular organization and its variations in human iPS cells. Nature 613 (7943), 345–354 (2023) . Micikevicius, P. et al. FP8 Formats for Deep Learning (2022). ArXiv:2209.05433 [cs]. (19) Han, S., Pool, J., Tran, J. & Dally, W. J. Learning both Weights and Connections for Efficient Neural Networks (2015). ArXiv:1506.02626 [cs] . (20) He, Y., Liu, P., Wang, Z., Hu, Z. & Yang, Y. Filter Pruning via Geometric Median for Deep Convolutional Neural Networks Acceleration (2018). ArXiv:1811.00250 [cs] . (21) Weigert, M. et al. Content-aware image restoration: pushing the limits of fluorescence microscopy. Nature Methods 15 (12), 1090–1097 (2018) . (22) Cardoso, M. J. et al. MONAI: An open-source framework for deep learning in healthcare (2022). ArXiv:2211.02701 [cs] . (23) Cardiff, R. D., Miller, C. H. & Munn, R. J. Manual Hematoxylin and Eosin Staining of Mouse Tissue Sections. Cold Spring Harbor Protocols 2014 (6), pdb.prot073411 (2014) . (24) Tomasovic, A. et al. Interference with ERK-dimerization at the nucleocytosolic interface targets pathological ERK1/2 signaling without cardiotoxic side-effects. Nature Communications 11 (1), 1733 (2020) . (25) Lorenz, K., Schmitt, J. P., Schmitteckert, E. M. & Lohse, M. J. A new type of ERK1/2 autophosphorylation causes cardiac hypertrophy. Nature Medicine 15 (1), 75–83 (2009) . (26) Grüneboom, A. et al. A network of trans-cortical capillaries as mainstay for blood circulation in long bones. Nature Metabolism 1 (2), 236–250 (2019) . (27) Ounkomol, C., Seshamani, S., Maleckar, M. M., Collman, F. & Johnson, G. R. Label-free prediction of three-dimensional fluorescence images from transmitted-light microscopy. Nature Methods 15 (11), 917–920 (2018) . (28) Viana, M. P. et al. Integrated intracellular organization and its variations in human iPS cells. Nature 613 (7943), 345–354 (2023) . Han, S., Pool, J., Tran, J. & Dally, W. J. Learning both Weights and Connections for Efficient Neural Networks (2015). ArXiv:1506.02626 [cs] . (20) He, Y., Liu, P., Wang, Z., Hu, Z. & Yang, Y. Filter Pruning via Geometric Median for Deep Convolutional Neural Networks Acceleration (2018). ArXiv:1811.00250 [cs] . (21) Weigert, M. et al. Content-aware image restoration: pushing the limits of fluorescence microscopy. Nature Methods 15 (12), 1090–1097 (2018) . (22) Cardoso, M. J. et al. MONAI: An open-source framework for deep learning in healthcare (2022). ArXiv:2211.02701 [cs] . (23) Cardiff, R. D., Miller, C. H. & Munn, R. J. Manual Hematoxylin and Eosin Staining of Mouse Tissue Sections. Cold Spring Harbor Protocols 2014 (6), pdb.prot073411 (2014) . (24) Tomasovic, A. et al. Interference with ERK-dimerization at the nucleocytosolic interface targets pathological ERK1/2 signaling without cardiotoxic side-effects. Nature Communications 11 (1), 1733 (2020) . (25) Lorenz, K., Schmitt, J. P., Schmitteckert, E. M. & Lohse, M. J. A new type of ERK1/2 autophosphorylation causes cardiac hypertrophy. Nature Medicine 15 (1), 75–83 (2009) . (26) Grüneboom, A. et al. A network of trans-cortical capillaries as mainstay for blood circulation in long bones. Nature Metabolism 1 (2), 236–250 (2019) . (27) Ounkomol, C., Seshamani, S., Maleckar, M. M., Collman, F. & Johnson, G. R. Label-free prediction of three-dimensional fluorescence images from transmitted-light microscopy. Nature Methods 15 (11), 917–920 (2018) . (28) Viana, M. P. et al. Integrated intracellular organization and its variations in human iPS cells. Nature 613 (7943), 345–354 (2023) . He, Y., Liu, P., Wang, Z., Hu, Z. & Yang, Y. Filter Pruning via Geometric Median for Deep Convolutional Neural Networks Acceleration (2018). ArXiv:1811.00250 [cs] . (21) Weigert, M. et al. Content-aware image restoration: pushing the limits of fluorescence microscopy. Nature Methods 15 (12), 1090–1097 (2018) . (22) Cardoso, M. J. et al. MONAI: An open-source framework for deep learning in healthcare (2022). ArXiv:2211.02701 [cs] . (23) Cardiff, R. D., Miller, C. H. & Munn, R. J. Manual Hematoxylin and Eosin Staining of Mouse Tissue Sections. Cold Spring Harbor Protocols 2014 (6), pdb.prot073411 (2014) . (24) Tomasovic, A. et al. Interference with ERK-dimerization at the nucleocytosolic interface targets pathological ERK1/2 signaling without cardiotoxic side-effects. Nature Communications 11 (1), 1733 (2020) . (25) Lorenz, K., Schmitt, J. P., Schmitteckert, E. M. & Lohse, M. J. A new type of ERK1/2 autophosphorylation causes cardiac hypertrophy. Nature Medicine 15 (1), 75–83 (2009) . (26) Grüneboom, A. et al. A network of trans-cortical capillaries as mainstay for blood circulation in long bones. Nature Metabolism 1 (2), 236–250 (2019) . (27) Ounkomol, C., Seshamani, S., Maleckar, M. M., Collman, F. & Johnson, G. R. Label-free prediction of three-dimensional fluorescence images from transmitted-light microscopy. Nature Methods 15 (11), 917–920 (2018) . (28) Viana, M. P. et al. Integrated intracellular organization and its variations in human iPS cells. Nature 613 (7943), 345–354 (2023) . Weigert, M. et al. Content-aware image restoration: pushing the limits of fluorescence microscopy. Nature Methods 15 (12), 1090–1097 (2018) . (22) Cardoso, M. J. et al. MONAI: An open-source framework for deep learning in healthcare (2022). ArXiv:2211.02701 [cs] . (23) Cardiff, R. D., Miller, C. H. & Munn, R. J. Manual Hematoxylin and Eosin Staining of Mouse Tissue Sections. Cold Spring Harbor Protocols 2014 (6), pdb.prot073411 (2014) . (24) Tomasovic, A. et al. Interference with ERK-dimerization at the nucleocytosolic interface targets pathological ERK1/2 signaling without cardiotoxic side-effects. Nature Communications 11 (1), 1733 (2020) . (25) Lorenz, K., Schmitt, J. P., Schmitteckert, E. M. & Lohse, M. J. A new type of ERK1/2 autophosphorylation causes cardiac hypertrophy. Nature Medicine 15 (1), 75–83 (2009) . (26) Grüneboom, A. et al. A network of trans-cortical capillaries as mainstay for blood circulation in long bones. Nature Metabolism 1 (2), 236–250 (2019) . (27) Ounkomol, C., Seshamani, S., Maleckar, M. M., Collman, F. & Johnson, G. R. Label-free prediction of three-dimensional fluorescence images from transmitted-light microscopy. Nature Methods 15 (11), 917–920 (2018) . (28) Viana, M. P. et al. Integrated intracellular organization and its variations in human iPS cells. Nature 613 (7943), 345–354 (2023) . Cardoso, M. J. et al. MONAI: An open-source framework for deep learning in healthcare (2022). ArXiv:2211.02701 [cs] . (23) Cardiff, R. D., Miller, C. H. & Munn, R. J. Manual Hematoxylin and Eosin Staining of Mouse Tissue Sections. Cold Spring Harbor Protocols 2014 (6), pdb.prot073411 (2014) . (24) Tomasovic, A. et al. Interference with ERK-dimerization at the nucleocytosolic interface targets pathological ERK1/2 signaling without cardiotoxic side-effects. Nature Communications 11 (1), 1733 (2020) . (25) Lorenz, K., Schmitt, J. P., Schmitteckert, E. M. & Lohse, M. J. A new type of ERK1/2 autophosphorylation causes cardiac hypertrophy. Nature Medicine 15 (1), 75–83 (2009) . (26) Grüneboom, A. et al. A network of trans-cortical capillaries as mainstay for blood circulation in long bones. Nature Metabolism 1 (2), 236–250 (2019) . (27) Ounkomol, C., Seshamani, S., Maleckar, M. M., Collman, F. & Johnson, G. R. Label-free prediction of three-dimensional fluorescence images from transmitted-light microscopy. Nature Methods 15 (11), 917–920 (2018) . (28) Viana, M. P. et al. Integrated intracellular organization and its variations in human iPS cells. Nature 613 (7943), 345–354 (2023) . Cardiff, R. D., Miller, C. H. & Munn, R. J. Manual Hematoxylin and Eosin Staining of Mouse Tissue Sections. Cold Spring Harbor Protocols 2014 (6), pdb.prot073411 (2014) . (24) Tomasovic, A. et al. Interference with ERK-dimerization at the nucleocytosolic interface targets pathological ERK1/2 signaling without cardiotoxic side-effects. Nature Communications 11 (1), 1733 (2020) . (25) Lorenz, K., Schmitt, J. P., Schmitteckert, E. M. & Lohse, M. J. A new type of ERK1/2 autophosphorylation causes cardiac hypertrophy. Nature Medicine 15 (1), 75–83 (2009) . (26) Grüneboom, A. et al. A network of trans-cortical capillaries as mainstay for blood circulation in long bones. Nature Metabolism 1 (2), 236–250 (2019) . (27) Ounkomol, C., Seshamani, S., Maleckar, M. M., Collman, F. & Johnson, G. R. Label-free prediction of three-dimensional fluorescence images from transmitted-light microscopy. Nature Methods 15 (11), 917–920 (2018) . (28) Viana, M. P. et al. Integrated intracellular organization and its variations in human iPS cells. Nature 613 (7943), 345–354 (2023) . Tomasovic, A. et al. Interference with ERK-dimerization at the nucleocytosolic interface targets pathological ERK1/2 signaling without cardiotoxic side-effects. Nature Communications 11 (1), 1733 (2020) . (25) Lorenz, K., Schmitt, J. P., Schmitteckert, E. M. & Lohse, M. J. A new type of ERK1/2 autophosphorylation causes cardiac hypertrophy. Nature Medicine 15 (1), 75–83 (2009) . (26) Grüneboom, A. et al. A network of trans-cortical capillaries as mainstay for blood circulation in long bones. Nature Metabolism 1 (2), 236–250 (2019) . (27) Ounkomol, C., Seshamani, S., Maleckar, M. M., Collman, F. & Johnson, G. R. Label-free prediction of three-dimensional fluorescence images from transmitted-light microscopy. Nature Methods 15 (11), 917–920 (2018) . (28) Viana, M. P. et al. Integrated intracellular organization and its variations in human iPS cells. Nature 613 (7943), 345–354 (2023) . Lorenz, K., Schmitt, J. P., Schmitteckert, E. M. & Lohse, M. J. A new type of ERK1/2 autophosphorylation causes cardiac hypertrophy. Nature Medicine 15 (1), 75–83 (2009) . (26) Grüneboom, A. et al. A network of trans-cortical capillaries as mainstay for blood circulation in long bones. Nature Metabolism 1 (2), 236–250 (2019) . (27) Ounkomol, C., Seshamani, S., Maleckar, M. M., Collman, F. & Johnson, G. R. Label-free prediction of three-dimensional fluorescence images from transmitted-light microscopy. Nature Methods 15 (11), 917–920 (2018) . (28) Viana, M. P. et al. Integrated intracellular organization and its variations in human iPS cells. Nature 613 (7943), 345–354 (2023) . Grüneboom, A. et al. A network of trans-cortical capillaries as mainstay for blood circulation in long bones. Nature Metabolism 1 (2), 236–250 (2019) . (27) Ounkomol, C., Seshamani, S., Maleckar, M. M., Collman, F. & Johnson, G. R. Label-free prediction of three-dimensional fluorescence images from transmitted-light microscopy. Nature Methods 15 (11), 917–920 (2018) . (28) Viana, M. P. et al. Integrated intracellular organization and its variations in human iPS cells. Nature 613 (7943), 345–354 (2023) . Ounkomol, C., Seshamani, S., Maleckar, M. M., Collman, F. & Johnson, G. R. Label-free prediction of three-dimensional fluorescence images from transmitted-light microscopy. Nature Methods 15 (11), 917–920 (2018) . (28) Viana, M. P. et al. Integrated intracellular organization and its variations in human iPS cells. Nature 613 (7943), 345–354 (2023) . Viana, M. P. et al. Integrated intracellular organization and its variations in human iPS cells. Nature 613 (7943), 345–354 (2023) .
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Nature Metabolism 1 (2), 236–250 (2019) . (27) Ounkomol, C., Seshamani, S., Maleckar, M. M., Collman, F. & Johnson, G. R. Label-free prediction of three-dimensional fluorescence images from transmitted-light microscopy. Nature Methods 15 (11), 917–920 (2018) . (28) Viana, M. P. et al. Integrated intracellular organization and its variations in human iPS cells. Nature 613 (7943), 345–354 (2023) . Li, Y. et al. MQBench: Towards Reproducible and Deployable Model Quantization Benchmark. Neural Information Processing Systems (2021) . (15) Siddegowda, S. et al. Neural Network Quantization with AI Model Efficiency Toolkit (AIMET) (2022). ArXiv:2201.08442 [cs.LG]. (16) Microsoft. Neural Network Intelligence (2022). URL https://github.com/microsoft/nni. (17) AskariHemmat, M. et al. U-Net Fixed-Point Quantization for Medical Image Segmentation. Large-Scale Annotation of Biomedical Data and Expert Label Synthesis and Hardware Aware Learning for Medical Imaging and Computer Assisted Intervention 115–124 (2019) . (18) Micikevicius, P. et al. FP8 Formats for Deep Learning (2022). ArXiv:2209.05433 [cs]. (19) Han, S., Pool, J., Tran, J. & Dally, W. J. Learning both Weights and Connections for Efficient Neural Networks (2015). ArXiv:1506.02626 [cs] . (20) He, Y., Liu, P., Wang, Z., Hu, Z. & Yang, Y. Filter Pruning via Geometric Median for Deep Convolutional Neural Networks Acceleration (2018). ArXiv:1811.00250 [cs] . (21) Weigert, M. et al. Content-aware image restoration: pushing the limits of fluorescence microscopy. Nature Methods 15 (12), 1090–1097 (2018) . (22) Cardoso, M. J. et al. MONAI: An open-source framework for deep learning in healthcare (2022). ArXiv:2211.02701 [cs] . (23) Cardiff, R. D., Miller, C. H. & Munn, R. J. Manual Hematoxylin and Eosin Staining of Mouse Tissue Sections. 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Neural Network Quantization with AI Model Efficiency Toolkit (AIMET) (2022). ArXiv:2201.08442 [cs.LG]. (16) Microsoft. Neural Network Intelligence (2022). URL https://github.com/microsoft/nni. (17) AskariHemmat, M. et al. U-Net Fixed-Point Quantization for Medical Image Segmentation. Large-Scale Annotation of Biomedical Data and Expert Label Synthesis and Hardware Aware Learning for Medical Imaging and Computer Assisted Intervention 115–124 (2019) . (18) Micikevicius, P. et al. FP8 Formats for Deep Learning (2022). ArXiv:2209.05433 [cs]. (19) Han, S., Pool, J., Tran, J. & Dally, W. J. Learning both Weights and Connections for Efficient Neural Networks (2015). ArXiv:1506.02626 [cs] . (20) He, Y., Liu, P., Wang, Z., Hu, Z. & Yang, Y. Filter Pruning via Geometric Median for Deep Convolutional Neural Networks Acceleration (2018). ArXiv:1811.00250 [cs] . (21) Weigert, M. et al. Content-aware image restoration: pushing the limits of fluorescence microscopy. 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Label-free prediction of three-dimensional fluorescence images from transmitted-light microscopy. Nature Methods 15 (11), 917–920 (2018) . (28) Viana, M. P. et al. Integrated intracellular organization and its variations in human iPS cells. Nature 613 (7943), 345–354 (2023) . Microsoft. Neural Network Intelligence (2022). URL https://github.com/microsoft/nni. (17) AskariHemmat, M. et al. U-Net Fixed-Point Quantization for Medical Image Segmentation. Large-Scale Annotation of Biomedical Data and Expert Label Synthesis and Hardware Aware Learning for Medical Imaging and Computer Assisted Intervention 115–124 (2019) . (18) Micikevicius, P. et al. FP8 Formats for Deep Learning (2022). ArXiv:2209.05433 [cs]. (19) Han, S., Pool, J., Tran, J. & Dally, W. J. Learning both Weights and Connections for Efficient Neural Networks (2015). ArXiv:1506.02626 [cs] . (20) He, Y., Liu, P., Wang, Z., Hu, Z. & Yang, Y. Filter Pruning via Geometric Median for Deep Convolutional Neural Networks Acceleration (2018). ArXiv:1811.00250 [cs] . (21) Weigert, M. et al. Content-aware image restoration: pushing the limits of fluorescence microscopy. Nature Methods 15 (12), 1090–1097 (2018) . (22) Cardoso, M. J. et al. MONAI: An open-source framework for deep learning in healthcare (2022). ArXiv:2211.02701 [cs] . (23) Cardiff, R. D., Miller, C. H. & Munn, R. J. Manual Hematoxylin and Eosin Staining of Mouse Tissue Sections. Cold Spring Harbor Protocols 2014 (6), pdb.prot073411 (2014) . (24) Tomasovic, A. et al. Interference with ERK-dimerization at the nucleocytosolic interface targets pathological ERK1/2 signaling without cardiotoxic side-effects. Nature Communications 11 (1), 1733 (2020) . (25) Lorenz, K., Schmitt, J. P., Schmitteckert, E. M. & Lohse, M. J. A new type of ERK1/2 autophosphorylation causes cardiac hypertrophy. Nature Medicine 15 (1), 75–83 (2009) . (26) Grüneboom, A. et al. A network of trans-cortical capillaries as mainstay for blood circulation in long bones. Nature Metabolism 1 (2), 236–250 (2019) . (27) Ounkomol, C., Seshamani, S., Maleckar, M. M., Collman, F. & Johnson, G. R. Label-free prediction of three-dimensional fluorescence images from transmitted-light microscopy. Nature Methods 15 (11), 917–920 (2018) . (28) Viana, M. P. et al. Integrated intracellular organization and its variations in human iPS cells. Nature 613 (7943), 345–354 (2023) . AskariHemmat, M. et al. U-Net Fixed-Point Quantization for Medical Image Segmentation. Large-Scale Annotation of Biomedical Data and Expert Label Synthesis and Hardware Aware Learning for Medical Imaging and Computer Assisted Intervention 115–124 (2019) . (18) Micikevicius, P. et al. FP8 Formats for Deep Learning (2022). ArXiv:2209.05433 [cs]. (19) Han, S., Pool, J., Tran, J. & Dally, W. J. Learning both Weights and Connections for Efficient Neural Networks (2015). ArXiv:1506.02626 [cs] . (20) He, Y., Liu, P., Wang, Z., Hu, Z. & Yang, Y. Filter Pruning via Geometric Median for Deep Convolutional Neural Networks Acceleration (2018). ArXiv:1811.00250 [cs] . (21) Weigert, M. et al. Content-aware image restoration: pushing the limits of fluorescence microscopy. Nature Methods 15 (12), 1090–1097 (2018) . (22) Cardoso, M. J. et al. MONAI: An open-source framework for deep learning in healthcare (2022). ArXiv:2211.02701 [cs] . (23) Cardiff, R. D., Miller, C. H. & Munn, R. J. Manual Hematoxylin and Eosin Staining of Mouse Tissue Sections. Cold Spring Harbor Protocols 2014 (6), pdb.prot073411 (2014) . (24) Tomasovic, A. et al. Interference with ERK-dimerization at the nucleocytosolic interface targets pathological ERK1/2 signaling without cardiotoxic side-effects. Nature Communications 11 (1), 1733 (2020) . (25) Lorenz, K., Schmitt, J. P., Schmitteckert, E. M. & Lohse, M. J. A new type of ERK1/2 autophosphorylation causes cardiac hypertrophy. Nature Medicine 15 (1), 75–83 (2009) . (26) Grüneboom, A. et al. A network of trans-cortical capillaries as mainstay for blood circulation in long bones. Nature Metabolism 1 (2), 236–250 (2019) . (27) Ounkomol, C., Seshamani, S., Maleckar, M. M., Collman, F. & Johnson, G. R. Label-free prediction of three-dimensional fluorescence images from transmitted-light microscopy. Nature Methods 15 (11), 917–920 (2018) . (28) Viana, M. P. et al. Integrated intracellular organization and its variations in human iPS cells. Nature 613 (7943), 345–354 (2023) . Micikevicius, P. et al. FP8 Formats for Deep Learning (2022). ArXiv:2209.05433 [cs]. (19) Han, S., Pool, J., Tran, J. & Dally, W. J. Learning both Weights and Connections for Efficient Neural Networks (2015). ArXiv:1506.02626 [cs] . (20) He, Y., Liu, P., Wang, Z., Hu, Z. & Yang, Y. Filter Pruning via Geometric Median for Deep Convolutional Neural Networks Acceleration (2018). ArXiv:1811.00250 [cs] . (21) Weigert, M. et al. Content-aware image restoration: pushing the limits of fluorescence microscopy. Nature Methods 15 (12), 1090–1097 (2018) . (22) Cardoso, M. J. et al. MONAI: An open-source framework for deep learning in healthcare (2022). ArXiv:2211.02701 [cs] . (23) Cardiff, R. D., Miller, C. H. & Munn, R. J. Manual Hematoxylin and Eosin Staining of Mouse Tissue Sections. Cold Spring Harbor Protocols 2014 (6), pdb.prot073411 (2014) . (24) Tomasovic, A. et al. Interference with ERK-dimerization at the nucleocytosolic interface targets pathological ERK1/2 signaling without cardiotoxic side-effects. Nature Communications 11 (1), 1733 (2020) . (25) Lorenz, K., Schmitt, J. P., Schmitteckert, E. M. & Lohse, M. J. A new type of ERK1/2 autophosphorylation causes cardiac hypertrophy. Nature Medicine 15 (1), 75–83 (2009) . (26) Grüneboom, A. et al. A network of trans-cortical capillaries as mainstay for blood circulation in long bones. Nature Metabolism 1 (2), 236–250 (2019) . (27) Ounkomol, C., Seshamani, S., Maleckar, M. M., Collman, F. & Johnson, G. R. Label-free prediction of three-dimensional fluorescence images from transmitted-light microscopy. Nature Methods 15 (11), 917–920 (2018) . (28) Viana, M. P. et al. Integrated intracellular organization and its variations in human iPS cells. Nature 613 (7943), 345–354 (2023) . Han, S., Pool, J., Tran, J. & Dally, W. J. Learning both Weights and Connections for Efficient Neural Networks (2015). ArXiv:1506.02626 [cs] . (20) He, Y., Liu, P., Wang, Z., Hu, Z. & Yang, Y. Filter Pruning via Geometric Median for Deep Convolutional Neural Networks Acceleration (2018). ArXiv:1811.00250 [cs] . (21) Weigert, M. et al. Content-aware image restoration: pushing the limits of fluorescence microscopy. Nature Methods 15 (12), 1090–1097 (2018) . (22) Cardoso, M. J. et al. MONAI: An open-source framework for deep learning in healthcare (2022). ArXiv:2211.02701 [cs] . (23) Cardiff, R. D., Miller, C. H. & Munn, R. J. Manual Hematoxylin and Eosin Staining of Mouse Tissue Sections. Cold Spring Harbor Protocols 2014 (6), pdb.prot073411 (2014) . (24) Tomasovic, A. et al. Interference with ERK-dimerization at the nucleocytosolic interface targets pathological ERK1/2 signaling without cardiotoxic side-effects. Nature Communications 11 (1), 1733 (2020) . (25) Lorenz, K., Schmitt, J. P., Schmitteckert, E. M. & Lohse, M. J. A new type of ERK1/2 autophosphorylation causes cardiac hypertrophy. Nature Medicine 15 (1), 75–83 (2009) . (26) Grüneboom, A. et al. A network of trans-cortical capillaries as mainstay for blood circulation in long bones. Nature Metabolism 1 (2), 236–250 (2019) . (27) Ounkomol, C., Seshamani, S., Maleckar, M. M., Collman, F. & Johnson, G. R. Label-free prediction of three-dimensional fluorescence images from transmitted-light microscopy. Nature Methods 15 (11), 917–920 (2018) . (28) Viana, M. P. et al. Integrated intracellular organization and its variations in human iPS cells. Nature 613 (7943), 345–354 (2023) . He, Y., Liu, P., Wang, Z., Hu, Z. & Yang, Y. Filter Pruning via Geometric Median for Deep Convolutional Neural Networks Acceleration (2018). ArXiv:1811.00250 [cs] . (21) Weigert, M. et al. Content-aware image restoration: pushing the limits of fluorescence microscopy. Nature Methods 15 (12), 1090–1097 (2018) . (22) Cardoso, M. J. et al. MONAI: An open-source framework for deep learning in healthcare (2022). ArXiv:2211.02701 [cs] . (23) Cardiff, R. D., Miller, C. H. & Munn, R. J. Manual Hematoxylin and Eosin Staining of Mouse Tissue Sections. Cold Spring Harbor Protocols 2014 (6), pdb.prot073411 (2014) . (24) Tomasovic, A. et al. Interference with ERK-dimerization at the nucleocytosolic interface targets pathological ERK1/2 signaling without cardiotoxic side-effects. Nature Communications 11 (1), 1733 (2020) . (25) Lorenz, K., Schmitt, J. P., Schmitteckert, E. M. & Lohse, M. J. A new type of ERK1/2 autophosphorylation causes cardiac hypertrophy. Nature Medicine 15 (1), 75–83 (2009) . (26) Grüneboom, A. et al. A network of trans-cortical capillaries as mainstay for blood circulation in long bones. Nature Metabolism 1 (2), 236–250 (2019) . (27) Ounkomol, C., Seshamani, S., Maleckar, M. M., Collman, F. & Johnson, G. R. Label-free prediction of three-dimensional fluorescence images from transmitted-light microscopy. Nature Methods 15 (11), 917–920 (2018) . (28) Viana, M. P. et al. Integrated intracellular organization and its variations in human iPS cells. Nature 613 (7943), 345–354 (2023) . Weigert, M. et al. Content-aware image restoration: pushing the limits of fluorescence microscopy. Nature Methods 15 (12), 1090–1097 (2018) . (22) Cardoso, M. J. et al. MONAI: An open-source framework for deep learning in healthcare (2022). ArXiv:2211.02701 [cs] . (23) Cardiff, R. D., Miller, C. H. & Munn, R. J. Manual Hematoxylin and Eosin Staining of Mouse Tissue Sections. Cold Spring Harbor Protocols 2014 (6), pdb.prot073411 (2014) . (24) Tomasovic, A. et al. Interference with ERK-dimerization at the nucleocytosolic interface targets pathological ERK1/2 signaling without cardiotoxic side-effects. Nature Communications 11 (1), 1733 (2020) . (25) Lorenz, K., Schmitt, J. P., Schmitteckert, E. M. & Lohse, M. J. A new type of ERK1/2 autophosphorylation causes cardiac hypertrophy. Nature Medicine 15 (1), 75–83 (2009) . (26) Grüneboom, A. et al. A network of trans-cortical capillaries as mainstay for blood circulation in long bones. Nature Metabolism 1 (2), 236–250 (2019) . (27) Ounkomol, C., Seshamani, S., Maleckar, M. M., Collman, F. & Johnson, G. R. Label-free prediction of three-dimensional fluorescence images from transmitted-light microscopy. Nature Methods 15 (11), 917–920 (2018) . (28) Viana, M. P. et al. Integrated intracellular organization and its variations in human iPS cells. Nature 613 (7943), 345–354 (2023) . Cardoso, M. J. et al. MONAI: An open-source framework for deep learning in healthcare (2022). ArXiv:2211.02701 [cs] . (23) Cardiff, R. D., Miller, C. H. & Munn, R. J. Manual Hematoxylin and Eosin Staining of Mouse Tissue Sections. Cold Spring Harbor Protocols 2014 (6), pdb.prot073411 (2014) . (24) Tomasovic, A. et al. Interference with ERK-dimerization at the nucleocytosolic interface targets pathological ERK1/2 signaling without cardiotoxic side-effects. Nature Communications 11 (1), 1733 (2020) . (25) Lorenz, K., Schmitt, J. P., Schmitteckert, E. M. & Lohse, M. J. A new type of ERK1/2 autophosphorylation causes cardiac hypertrophy. Nature Medicine 15 (1), 75–83 (2009) . (26) Grüneboom, A. et al. A network of trans-cortical capillaries as mainstay for blood circulation in long bones. Nature Metabolism 1 (2), 236–250 (2019) . (27) Ounkomol, C., Seshamani, S., Maleckar, M. M., Collman, F. & Johnson, G. R. Label-free prediction of three-dimensional fluorescence images from transmitted-light microscopy. Nature Methods 15 (11), 917–920 (2018) . (28) Viana, M. P. et al. Integrated intracellular organization and its variations in human iPS cells. Nature 613 (7943), 345–354 (2023) . Cardiff, R. D., Miller, C. H. & Munn, R. J. Manual Hematoxylin and Eosin Staining of Mouse Tissue Sections. Cold Spring Harbor Protocols 2014 (6), pdb.prot073411 (2014) . (24) Tomasovic, A. et al. Interference with ERK-dimerization at the nucleocytosolic interface targets pathological ERK1/2 signaling without cardiotoxic side-effects. Nature Communications 11 (1), 1733 (2020) . (25) Lorenz, K., Schmitt, J. P., Schmitteckert, E. M. & Lohse, M. J. A new type of ERK1/2 autophosphorylation causes cardiac hypertrophy. Nature Medicine 15 (1), 75–83 (2009) . (26) Grüneboom, A. et al. A network of trans-cortical capillaries as mainstay for blood circulation in long bones. Nature Metabolism 1 (2), 236–250 (2019) . (27) Ounkomol, C., Seshamani, S., Maleckar, M. M., Collman, F. & Johnson, G. R. Label-free prediction of three-dimensional fluorescence images from transmitted-light microscopy. Nature Methods 15 (11), 917–920 (2018) . (28) Viana, M. P. et al. Integrated intracellular organization and its variations in human iPS cells. Nature 613 (7943), 345–354 (2023) . Tomasovic, A. et al. Interference with ERK-dimerization at the nucleocytosolic interface targets pathological ERK1/2 signaling without cardiotoxic side-effects. Nature Communications 11 (1), 1733 (2020) . (25) Lorenz, K., Schmitt, J. P., Schmitteckert, E. M. & Lohse, M. J. A new type of ERK1/2 autophosphorylation causes cardiac hypertrophy. Nature Medicine 15 (1), 75–83 (2009) . (26) Grüneboom, A. et al. A network of trans-cortical capillaries as mainstay for blood circulation in long bones. Nature Metabolism 1 (2), 236–250 (2019) . (27) Ounkomol, C., Seshamani, S., Maleckar, M. M., Collman, F. & Johnson, G. R. Label-free prediction of three-dimensional fluorescence images from transmitted-light microscopy. Nature Methods 15 (11), 917–920 (2018) . (28) Viana, M. P. et al. Integrated intracellular organization and its variations in human iPS cells. Nature 613 (7943), 345–354 (2023) . Lorenz, K., Schmitt, J. P., Schmitteckert, E. M. & Lohse, M. J. A new type of ERK1/2 autophosphorylation causes cardiac hypertrophy. Nature Medicine 15 (1), 75–83 (2009) . (26) Grüneboom, A. et al. A network of trans-cortical capillaries as mainstay for blood circulation in long bones. Nature Metabolism 1 (2), 236–250 (2019) . (27) Ounkomol, C., Seshamani, S., Maleckar, M. M., Collman, F. & Johnson, G. R. Label-free prediction of three-dimensional fluorescence images from transmitted-light microscopy. Nature Methods 15 (11), 917–920 (2018) . (28) Viana, M. P. et al. Integrated intracellular organization and its variations in human iPS cells. Nature 613 (7943), 345–354 (2023) . Grüneboom, A. et al. A network of trans-cortical capillaries as mainstay for blood circulation in long bones. Nature Metabolism 1 (2), 236–250 (2019) . (27) Ounkomol, C., Seshamani, S., Maleckar, M. M., Collman, F. & Johnson, G. R. Label-free prediction of three-dimensional fluorescence images from transmitted-light microscopy. Nature Methods 15 (11), 917–920 (2018) . (28) Viana, M. P. et al. Integrated intracellular organization and its variations in human iPS cells. Nature 613 (7943), 345–354 (2023) . Ounkomol, C., Seshamani, S., Maleckar, M. M., Collman, F. & Johnson, G. R. Label-free prediction of three-dimensional fluorescence images from transmitted-light microscopy. Nature Methods 15 (11), 917–920 (2018) . (28) Viana, M. P. et al. Integrated intracellular organization and its variations in human iPS cells. Nature 613 (7943), 345–354 (2023) . Viana, M. P. et al. Integrated intracellular organization and its variations in human iPS cells. Nature 613 (7943), 345–354 (2023) .
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Nature 613 (7943), 345–354 (2023) . Siddegowda, S. et al. Neural Network Quantization with AI Model Efficiency Toolkit (AIMET) (2022). ArXiv:2201.08442 [cs.LG]. (16) Microsoft. Neural Network Intelligence (2022). URL https://github.com/microsoft/nni. (17) AskariHemmat, M. et al. U-Net Fixed-Point Quantization for Medical Image Segmentation. Large-Scale Annotation of Biomedical Data and Expert Label Synthesis and Hardware Aware Learning for Medical Imaging and Computer Assisted Intervention 115–124 (2019) . (18) Micikevicius, P. et al. FP8 Formats for Deep Learning (2022). ArXiv:2209.05433 [cs]. (19) Han, S., Pool, J., Tran, J. & Dally, W. J. Learning both Weights and Connections for Efficient Neural Networks (2015). ArXiv:1506.02626 [cs] . (20) He, Y., Liu, P., Wang, Z., Hu, Z. & Yang, Y. Filter Pruning via Geometric Median for Deep Convolutional Neural Networks Acceleration (2018). ArXiv:1811.00250 [cs] . (21) Weigert, M. et al. Content-aware image restoration: pushing the limits of fluorescence microscopy. Nature Methods 15 (12), 1090–1097 (2018) . (22) Cardoso, M. J. et al. MONAI: An open-source framework for deep learning in healthcare (2022). ArXiv:2211.02701 [cs] . (23) Cardiff, R. D., Miller, C. H. & Munn, R. J. Manual Hematoxylin and Eosin Staining of Mouse Tissue Sections. Cold Spring Harbor Protocols 2014 (6), pdb.prot073411 (2014) . (24) Tomasovic, A. et al. Interference with ERK-dimerization at the nucleocytosolic interface targets pathological ERK1/2 signaling without cardiotoxic side-effects. Nature Communications 11 (1), 1733 (2020) . (25) Lorenz, K., Schmitt, J. P., Schmitteckert, E. M. & Lohse, M. J. A new type of ERK1/2 autophosphorylation causes cardiac hypertrophy. Nature Medicine 15 (1), 75–83 (2009) . (26) Grüneboom, A. et al. A network of trans-cortical capillaries as mainstay for blood circulation in long bones. Nature Metabolism 1 (2), 236–250 (2019) . (27) Ounkomol, C., Seshamani, S., Maleckar, M. M., Collman, F. & Johnson, G. R. Label-free prediction of three-dimensional fluorescence images from transmitted-light microscopy. Nature Methods 15 (11), 917–920 (2018) . (28) Viana, M. P. et al. Integrated intracellular organization and its variations in human iPS cells. Nature 613 (7943), 345–354 (2023) . Microsoft. Neural Network Intelligence (2022). URL https://github.com/microsoft/nni. (17) AskariHemmat, M. et al. U-Net Fixed-Point Quantization for Medical Image Segmentation. Large-Scale Annotation of Biomedical Data and Expert Label Synthesis and Hardware Aware Learning for Medical Imaging and Computer Assisted Intervention 115–124 (2019) . (18) Micikevicius, P. et al. FP8 Formats for Deep Learning (2022). ArXiv:2209.05433 [cs]. (19) Han, S., Pool, J., Tran, J. & Dally, W. J. Learning both Weights and Connections for Efficient Neural Networks (2015). ArXiv:1506.02626 [cs] . (20) He, Y., Liu, P., Wang, Z., Hu, Z. & Yang, Y. 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(20) He, Y., Liu, P., Wang, Z., Hu, Z. & Yang, Y. Filter Pruning via Geometric Median for Deep Convolutional Neural Networks Acceleration (2018). ArXiv:1811.00250 [cs] . (21) Weigert, M. et al. Content-aware image restoration: pushing the limits of fluorescence microscopy. Nature Methods 15 (12), 1090–1097 (2018) . (22) Cardoso, M. J. et al. MONAI: An open-source framework for deep learning in healthcare (2022). ArXiv:2211.02701 [cs] . (23) Cardiff, R. D., Miller, C. H. & Munn, R. J. Manual Hematoxylin and Eosin Staining of Mouse Tissue Sections. Cold Spring Harbor Protocols 2014 (6), pdb.prot073411 (2014) . (24) Tomasovic, A. et al. Interference with ERK-dimerization at the nucleocytosolic interface targets pathological ERK1/2 signaling without cardiotoxic side-effects. Nature Communications 11 (1), 1733 (2020) . (25) Lorenz, K., Schmitt, J. P., Schmitteckert, E. M. & Lohse, M. J. A new type of ERK1/2 autophosphorylation causes cardiac hypertrophy. Nature Medicine 15 (1), 75–83 (2009) . (26) Grüneboom, A. et al. A network of trans-cortical capillaries as mainstay for blood circulation in long bones. Nature Metabolism 1 (2), 236–250 (2019) . (27) Ounkomol, C., Seshamani, S., Maleckar, M. M., Collman, F. & Johnson, G. R. Label-free prediction of three-dimensional fluorescence images from transmitted-light microscopy. Nature Methods 15 (11), 917–920 (2018) . (28) Viana, M. P. et al. Integrated intracellular organization and its variations in human iPS cells. Nature 613 (7943), 345–354 (2023) . Micikevicius, P. et al. FP8 Formats for Deep Learning (2022). ArXiv:2209.05433 [cs]. (19) Han, S., Pool, J., Tran, J. & Dally, W. J. Learning both Weights and Connections for Efficient Neural Networks (2015). ArXiv:1506.02626 [cs] . (20) He, Y., Liu, P., Wang, Z., Hu, Z. & Yang, Y. Filter Pruning via Geometric Median for Deep Convolutional Neural Networks Acceleration (2018). ArXiv:1811.00250 [cs] . (21) Weigert, M. et al. Content-aware image restoration: pushing the limits of fluorescence microscopy. Nature Methods 15 (12), 1090–1097 (2018) . (22) Cardoso, M. J. et al. MONAI: An open-source framework for deep learning in healthcare (2022). ArXiv:2211.02701 [cs] . (23) Cardiff, R. D., Miller, C. H. & Munn, R. J. Manual Hematoxylin and Eosin Staining of Mouse Tissue Sections. Cold Spring Harbor Protocols 2014 (6), pdb.prot073411 (2014) . (24) Tomasovic, A. et al. Interference with ERK-dimerization at the nucleocytosolic interface targets pathological ERK1/2 signaling without cardiotoxic side-effects. Nature Communications 11 (1), 1733 (2020) . (25) Lorenz, K., Schmitt, J. P., Schmitteckert, E. M. & Lohse, M. J. A new type of ERK1/2 autophosphorylation causes cardiac hypertrophy. Nature Medicine 15 (1), 75–83 (2009) . (26) Grüneboom, A. et al. A network of trans-cortical capillaries as mainstay for blood circulation in long bones. Nature Metabolism 1 (2), 236–250 (2019) . (27) Ounkomol, C., Seshamani, S., Maleckar, M. M., Collman, F. & Johnson, G. R. Label-free prediction of three-dimensional fluorescence images from transmitted-light microscopy. Nature Methods 15 (11), 917–920 (2018) . (28) Viana, M. P. et al. Integrated intracellular organization and its variations in human iPS cells. Nature 613 (7943), 345–354 (2023) . Han, S., Pool, J., Tran, J. & Dally, W. J. Learning both Weights and Connections for Efficient Neural Networks (2015). ArXiv:1506.02626 [cs] . (20) He, Y., Liu, P., Wang, Z., Hu, Z. & Yang, Y. Filter Pruning via Geometric Median for Deep Convolutional Neural Networks Acceleration (2018). ArXiv:1811.00250 [cs] . (21) Weigert, M. et al. Content-aware image restoration: pushing the limits of fluorescence microscopy. Nature Methods 15 (12), 1090–1097 (2018) . (22) Cardoso, M. J. et al. MONAI: An open-source framework for deep learning in healthcare (2022). ArXiv:2211.02701 [cs] . (23) Cardiff, R. D., Miller, C. H. & Munn, R. J. Manual Hematoxylin and Eosin Staining of Mouse Tissue Sections. Cold Spring Harbor Protocols 2014 (6), pdb.prot073411 (2014) . (24) Tomasovic, A. et al. Interference with ERK-dimerization at the nucleocytosolic interface targets pathological ERK1/2 signaling without cardiotoxic side-effects. Nature Communications 11 (1), 1733 (2020) . (25) Lorenz, K., Schmitt, J. P., Schmitteckert, E. M. & Lohse, M. J. A new type of ERK1/2 autophosphorylation causes cardiac hypertrophy. Nature Medicine 15 (1), 75–83 (2009) . (26) Grüneboom, A. et al. A network of trans-cortical capillaries as mainstay for blood circulation in long bones. Nature Metabolism 1 (2), 236–250 (2019) . (27) Ounkomol, C., Seshamani, S., Maleckar, M. M., Collman, F. & Johnson, G. R. Label-free prediction of three-dimensional fluorescence images from transmitted-light microscopy. Nature Methods 15 (11), 917–920 (2018) . (28) Viana, M. P. et al. Integrated intracellular organization and its variations in human iPS cells. Nature 613 (7943), 345–354 (2023) . He, Y., Liu, P., Wang, Z., Hu, Z. & Yang, Y. Filter Pruning via Geometric Median for Deep Convolutional Neural Networks Acceleration (2018). ArXiv:1811.00250 [cs] . (21) Weigert, M. et al. Content-aware image restoration: pushing the limits of fluorescence microscopy. Nature Methods 15 (12), 1090–1097 (2018) . (22) Cardoso, M. J. et al. MONAI: An open-source framework for deep learning in healthcare (2022). ArXiv:2211.02701 [cs] . (23) Cardiff, R. D., Miller, C. H. & Munn, R. J. Manual Hematoxylin and Eosin Staining of Mouse Tissue Sections. Cold Spring Harbor Protocols 2014 (6), pdb.prot073411 (2014) . (24) Tomasovic, A. et al. Interference with ERK-dimerization at the nucleocytosolic interface targets pathological ERK1/2 signaling without cardiotoxic side-effects. Nature Communications 11 (1), 1733 (2020) . (25) Lorenz, K., Schmitt, J. P., Schmitteckert, E. M. & Lohse, M. J. A new type of ERK1/2 autophosphorylation causes cardiac hypertrophy. Nature Medicine 15 (1), 75–83 (2009) . (26) Grüneboom, A. et al. A network of trans-cortical capillaries as mainstay for blood circulation in long bones. Nature Metabolism 1 (2), 236–250 (2019) . (27) Ounkomol, C., Seshamani, S., Maleckar, M. M., Collman, F. & Johnson, G. R. Label-free prediction of three-dimensional fluorescence images from transmitted-light microscopy. Nature Methods 15 (11), 917–920 (2018) . (28) Viana, M. P. et al. Integrated intracellular organization and its variations in human iPS cells. Nature 613 (7943), 345–354 (2023) . Weigert, M. et al. Content-aware image restoration: pushing the limits of fluorescence microscopy. Nature Methods 15 (12), 1090–1097 (2018) . (22) Cardoso, M. J. et al. MONAI: An open-source framework for deep learning in healthcare (2022). ArXiv:2211.02701 [cs] . (23) Cardiff, R. D., Miller, C. H. & Munn, R. J. Manual Hematoxylin and Eosin Staining of Mouse Tissue Sections. Cold Spring Harbor Protocols 2014 (6), pdb.prot073411 (2014) . (24) Tomasovic, A. et al. Interference with ERK-dimerization at the nucleocytosolic interface targets pathological ERK1/2 signaling without cardiotoxic side-effects. Nature Communications 11 (1), 1733 (2020) . (25) Lorenz, K., Schmitt, J. P., Schmitteckert, E. M. & Lohse, M. J. A new type of ERK1/2 autophosphorylation causes cardiac hypertrophy. Nature Medicine 15 (1), 75–83 (2009) . (26) Grüneboom, A. et al. A network of trans-cortical capillaries as mainstay for blood circulation in long bones. Nature Metabolism 1 (2), 236–250 (2019) . (27) Ounkomol, C., Seshamani, S., Maleckar, M. M., Collman, F. & Johnson, G. R. Label-free prediction of three-dimensional fluorescence images from transmitted-light microscopy. Nature Methods 15 (11), 917–920 (2018) . (28) Viana, M. P. et al. Integrated intracellular organization and its variations in human iPS cells. Nature 613 (7943), 345–354 (2023) . Cardoso, M. J. et al. MONAI: An open-source framework for deep learning in healthcare (2022). 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Integrated intracellular organization and its variations in human iPS cells. Nature 613 (7943), 345–354 (2023) . Cardiff, R. D., Miller, C. H. & Munn, R. J. Manual Hematoxylin and Eosin Staining of Mouse Tissue Sections. Cold Spring Harbor Protocols 2014 (6), pdb.prot073411 (2014) . (24) Tomasovic, A. et al. Interference with ERK-dimerization at the nucleocytosolic interface targets pathological ERK1/2 signaling without cardiotoxic side-effects. Nature Communications 11 (1), 1733 (2020) . (25) Lorenz, K., Schmitt, J. P., Schmitteckert, E. M. & Lohse, M. J. A new type of ERK1/2 autophosphorylation causes cardiac hypertrophy. Nature Medicine 15 (1), 75–83 (2009) . (26) Grüneboom, A. et al. A network of trans-cortical capillaries as mainstay for blood circulation in long bones. Nature Metabolism 1 (2), 236–250 (2019) . (27) Ounkomol, C., Seshamani, S., Maleckar, M. M., Collman, F. & Johnson, G. R. Label-free prediction of three-dimensional fluorescence images from transmitted-light microscopy. Nature Methods 15 (11), 917–920 (2018) . (28) Viana, M. P. et al. Integrated intracellular organization and its variations in human iPS cells. Nature 613 (7943), 345–354 (2023) . Tomasovic, A. et al. Interference with ERK-dimerization at the nucleocytosolic interface targets pathological ERK1/2 signaling without cardiotoxic side-effects. Nature Communications 11 (1), 1733 (2020) . (25) Lorenz, K., Schmitt, J. P., Schmitteckert, E. M. & Lohse, M. J. A new type of ERK1/2 autophosphorylation causes cardiac hypertrophy. Nature Medicine 15 (1), 75–83 (2009) . (26) Grüneboom, A. et al. A network of trans-cortical capillaries as mainstay for blood circulation in long bones. Nature Metabolism 1 (2), 236–250 (2019) . (27) Ounkomol, C., Seshamani, S., Maleckar, M. M., Collman, F. & Johnson, G. R. Label-free prediction of three-dimensional fluorescence images from transmitted-light microscopy. 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Nature Metabolism 1 (2), 236–250 (2019) . (27) Ounkomol, C., Seshamani, S., Maleckar, M. M., Collman, F. & Johnson, G. R. Label-free prediction of three-dimensional fluorescence images from transmitted-light microscopy. Nature Methods 15 (11), 917–920 (2018) . (28) Viana, M. P. et al. Integrated intracellular organization and its variations in human iPS cells. Nature 613 (7943), 345–354 (2023) . Ounkomol, C., Seshamani, S., Maleckar, M. M., Collman, F. & Johnson, G. R. Label-free prediction of three-dimensional fluorescence images from transmitted-light microscopy. Nature Methods 15 (11), 917–920 (2018) . (28) Viana, M. P. et al. Integrated intracellular organization and its variations in human iPS cells. Nature 613 (7943), 345–354 (2023) . Viana, M. P. et al. Integrated intracellular organization and its variations in human iPS cells. Nature 613 (7943), 345–354 (2023) .
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Nature Communications 11 (1), 1733 (2020) . (25) Lorenz, K., Schmitt, J. P., Schmitteckert, E. M. & Lohse, M. J. A new type of ERK1/2 autophosphorylation causes cardiac hypertrophy. Nature Medicine 15 (1), 75–83 (2009) . (26) Grüneboom, A. et al. A network of trans-cortical capillaries as mainstay for blood circulation in long bones. Nature Metabolism 1 (2), 236–250 (2019) . (27) Ounkomol, C., Seshamani, S., Maleckar, M. M., Collman, F. & Johnson, G. R. Label-free prediction of three-dimensional fluorescence images from transmitted-light microscopy. Nature Methods 15 (11), 917–920 (2018) . (28) Viana, M. P. et al. Integrated intracellular organization and its variations in human iPS cells. Nature 613 (7943), 345–354 (2023) . Microsoft. Neural Network Intelligence (2022). URL https://github.com/microsoft/nni. (17) AskariHemmat, M. et al. U-Net Fixed-Point Quantization for Medical Image Segmentation. Large-Scale Annotation of Biomedical Data and Expert Label Synthesis and Hardware Aware Learning for Medical Imaging and Computer Assisted Intervention 115–124 (2019) . (18) Micikevicius, P. et al. FP8 Formats for Deep Learning (2022). ArXiv:2209.05433 [cs]. (19) Han, S., Pool, J., Tran, J. & Dally, W. J. Learning both Weights and Connections for Efficient Neural Networks (2015). ArXiv:1506.02626 [cs] . (20) He, Y., Liu, P., Wang, Z., Hu, Z. & Yang, Y. Filter Pruning via Geometric Median for Deep Convolutional Neural Networks Acceleration (2018). ArXiv:1811.00250 [cs] . (21) Weigert, M. et al. Content-aware image restoration: pushing the limits of fluorescence microscopy. Nature Methods 15 (12), 1090–1097 (2018) . (22) Cardoso, M. J. et al. MONAI: An open-source framework for deep learning in healthcare (2022). ArXiv:2211.02701 [cs] . (23) Cardiff, R. D., Miller, C. H. & Munn, R. J. Manual Hematoxylin and Eosin Staining of Mouse Tissue Sections. Cold Spring Harbor Protocols 2014 (6), pdb.prot073411 (2014) . (24) Tomasovic, A. et al. Interference with ERK-dimerization at the nucleocytosolic interface targets pathological ERK1/2 signaling without cardiotoxic side-effects. Nature Communications 11 (1), 1733 (2020) . (25) Lorenz, K., Schmitt, J. P., Schmitteckert, E. M. & Lohse, M. J. A new type of ERK1/2 autophosphorylation causes cardiac hypertrophy. Nature Medicine 15 (1), 75–83 (2009) . (26) Grüneboom, A. et al. A network of trans-cortical capillaries as mainstay for blood circulation in long bones. Nature Metabolism 1 (2), 236–250 (2019) . (27) Ounkomol, C., Seshamani, S., Maleckar, M. M., Collman, F. & Johnson, G. R. Label-free prediction of three-dimensional fluorescence images from transmitted-light microscopy. Nature Methods 15 (11), 917–920 (2018) . (28) Viana, M. P. et al. Integrated intracellular organization and its variations in human iPS cells. Nature 613 (7943), 345–354 (2023) . AskariHemmat, M. et al. U-Net Fixed-Point Quantization for Medical Image Segmentation. Large-Scale Annotation of Biomedical Data and Expert Label Synthesis and Hardware Aware Learning for Medical Imaging and Computer Assisted Intervention 115–124 (2019) . (18) Micikevicius, P. et al. FP8 Formats for Deep Learning (2022). ArXiv:2209.05433 [cs]. (19) Han, S., Pool, J., Tran, J. & Dally, W. J. Learning both Weights and Connections for Efficient Neural Networks (2015). ArXiv:1506.02626 [cs] . (20) He, Y., Liu, P., Wang, Z., Hu, Z. & Yang, Y. Filter Pruning via Geometric Median for Deep Convolutional Neural Networks Acceleration (2018). ArXiv:1811.00250 [cs] . (21) Weigert, M. et al. Content-aware image restoration: pushing the limits of fluorescence microscopy. Nature Methods 15 (12), 1090–1097 (2018) . (22) Cardoso, M. J. et al. MONAI: An open-source framework for deep learning in healthcare (2022). ArXiv:2211.02701 [cs] . (23) Cardiff, R. D., Miller, C. H. & Munn, R. J. 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(27) Ounkomol, C., Seshamani, S., Maleckar, M. M., Collman, F. & Johnson, G. R. Label-free prediction of three-dimensional fluorescence images from transmitted-light microscopy. Nature Methods 15 (11), 917–920 (2018) . (28) Viana, M. P. et al. Integrated intracellular organization and its variations in human iPS cells. Nature 613 (7943), 345–354 (2023) . Han, S., Pool, J., Tran, J. & Dally, W. J. Learning both Weights and Connections for Efficient Neural Networks (2015). ArXiv:1506.02626 [cs] . (20) He, Y., Liu, P., Wang, Z., Hu, Z. & Yang, Y. Filter Pruning via Geometric Median for Deep Convolutional Neural Networks Acceleration (2018). ArXiv:1811.00250 [cs] . (21) Weigert, M. et al. Content-aware image restoration: pushing the limits of fluorescence microscopy. Nature Methods 15 (12), 1090–1097 (2018) . (22) Cardoso, M. J. et al. MONAI: An open-source framework for deep learning in healthcare (2022). ArXiv:2211.02701 [cs] . (23) Cardiff, R. D., Miller, C. H. & Munn, R. J. 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Manual Hematoxylin and Eosin Staining of Mouse Tissue Sections. Cold Spring Harbor Protocols 2014 (6), pdb.prot073411 (2014) . (24) Tomasovic, A. et al. Interference with ERK-dimerization at the nucleocytosolic interface targets pathological ERK1/2 signaling without cardiotoxic side-effects. Nature Communications 11 (1), 1733 (2020) . (25) Lorenz, K., Schmitt, J. P., Schmitteckert, E. M. & Lohse, M. J. A new type of ERK1/2 autophosphorylation causes cardiac hypertrophy. Nature Medicine 15 (1), 75–83 (2009) . (26) Grüneboom, A. et al. A network of trans-cortical capillaries as mainstay for blood circulation in long bones. Nature Metabolism 1 (2), 236–250 (2019) . (27) Ounkomol, C., Seshamani, S., Maleckar, M. M., Collman, F. & Johnson, G. R. Label-free prediction of three-dimensional fluorescence images from transmitted-light microscopy. Nature Methods 15 (11), 917–920 (2018) . (28) Viana, M. P. et al. Integrated intracellular organization and its variations in human iPS cells. Nature 613 (7943), 345–354 (2023) . Micikevicius, P. et al. FP8 Formats for Deep Learning (2022). ArXiv:2209.05433 [cs]. (19) Han, S., Pool, J., Tran, J. & Dally, W. J. Learning both Weights and Connections for Efficient Neural Networks (2015). ArXiv:1506.02626 [cs] . (20) He, Y., Liu, P., Wang, Z., Hu, Z. & Yang, Y. Filter Pruning via Geometric Median for Deep Convolutional Neural Networks Acceleration (2018). ArXiv:1811.00250 [cs] . (21) Weigert, M. et al. Content-aware image restoration: pushing the limits of fluorescence microscopy. Nature Methods 15 (12), 1090–1097 (2018) . (22) Cardoso, M. J. et al. MONAI: An open-source framework for deep learning in healthcare (2022). ArXiv:2211.02701 [cs] . (23) Cardiff, R. D., Miller, C. H. & Munn, R. J. Manual Hematoxylin and Eosin Staining of Mouse Tissue Sections. Cold Spring Harbor Protocols 2014 (6), pdb.prot073411 (2014) . (24) Tomasovic, A. et al. Interference with ERK-dimerization at the nucleocytosolic interface targets pathological ERK1/2 signaling without cardiotoxic side-effects. Nature Communications 11 (1), 1733 (2020) . (25) Lorenz, K., Schmitt, J. P., Schmitteckert, E. M. & Lohse, M. J. A new type of ERK1/2 autophosphorylation causes cardiac hypertrophy. Nature Medicine 15 (1), 75–83 (2009) . (26) Grüneboom, A. et al. A network of trans-cortical capillaries as mainstay for blood circulation in long bones. Nature Metabolism 1 (2), 236–250 (2019) . (27) Ounkomol, C., Seshamani, S., Maleckar, M. M., Collman, F. & Johnson, G. R. Label-free prediction of three-dimensional fluorescence images from transmitted-light microscopy. Nature Methods 15 (11), 917–920 (2018) . (28) Viana, M. P. et al. Integrated intracellular organization and its variations in human iPS cells. Nature 613 (7943), 345–354 (2023) . Han, S., Pool, J., Tran, J. & Dally, W. J. Learning both Weights and Connections for Efficient Neural Networks (2015). ArXiv:1506.02626 [cs] . (20) He, Y., Liu, P., Wang, Z., Hu, Z. & Yang, Y. Filter Pruning via Geometric Median for Deep Convolutional Neural Networks Acceleration (2018). ArXiv:1811.00250 [cs] . (21) Weigert, M. et al. Content-aware image restoration: pushing the limits of fluorescence microscopy. Nature Methods 15 (12), 1090–1097 (2018) . (22) Cardoso, M. J. et al. MONAI: An open-source framework for deep learning in healthcare (2022). ArXiv:2211.02701 [cs] . (23) Cardiff, R. D., Miller, C. H. & Munn, R. J. Manual Hematoxylin and Eosin Staining of Mouse Tissue Sections. Cold Spring Harbor Protocols 2014 (6), pdb.prot073411 (2014) . (24) Tomasovic, A. et al. Interference with ERK-dimerization at the nucleocytosolic interface targets pathological ERK1/2 signaling without cardiotoxic side-effects. Nature Communications 11 (1), 1733 (2020) . (25) Lorenz, K., Schmitt, J. P., Schmitteckert, E. M. & Lohse, M. J. A new type of ERK1/2 autophosphorylation causes cardiac hypertrophy. Nature Medicine 15 (1), 75–83 (2009) . (26) Grüneboom, A. et al. A network of trans-cortical capillaries as mainstay for blood circulation in long bones. Nature Metabolism 1 (2), 236–250 (2019) . (27) Ounkomol, C., Seshamani, S., Maleckar, M. M., Collman, F. & Johnson, G. R. Label-free prediction of three-dimensional fluorescence images from transmitted-light microscopy. Nature Methods 15 (11), 917–920 (2018) . (28) Viana, M. P. et al. Integrated intracellular organization and its variations in human iPS cells. Nature 613 (7943), 345–354 (2023) . He, Y., Liu, P., Wang, Z., Hu, Z. & Yang, Y. Filter Pruning via Geometric Median for Deep Convolutional Neural Networks Acceleration (2018). ArXiv:1811.00250 [cs] . (21) Weigert, M. et al. Content-aware image restoration: pushing the limits of fluorescence microscopy. Nature Methods 15 (12), 1090–1097 (2018) . (22) Cardoso, M. J. et al. MONAI: An open-source framework for deep learning in healthcare (2022). ArXiv:2211.02701 [cs] . (23) Cardiff, R. D., Miller, C. H. & Munn, R. J. Manual Hematoxylin and Eosin Staining of Mouse Tissue Sections. Cold Spring Harbor Protocols 2014 (6), pdb.prot073411 (2014) . (24) Tomasovic, A. et al. Interference with ERK-dimerization at the nucleocytosolic interface targets pathological ERK1/2 signaling without cardiotoxic side-effects. Nature Communications 11 (1), 1733 (2020) . (25) Lorenz, K., Schmitt, J. P., Schmitteckert, E. M. & Lohse, M. J. A new type of ERK1/2 autophosphorylation causes cardiac hypertrophy. Nature Medicine 15 (1), 75–83 (2009) . (26) Grüneboom, A. et al. A network of trans-cortical capillaries as mainstay for blood circulation in long bones. Nature Metabolism 1 (2), 236–250 (2019) . (27) Ounkomol, C., Seshamani, S., Maleckar, M. M., Collman, F. & Johnson, G. R. Label-free prediction of three-dimensional fluorescence images from transmitted-light microscopy. Nature Methods 15 (11), 917–920 (2018) . (28) Viana, M. P. et al. Integrated intracellular organization and its variations in human iPS cells. Nature 613 (7943), 345–354 (2023) . Weigert, M. et al. Content-aware image restoration: pushing the limits of fluorescence microscopy. Nature Methods 15 (12), 1090–1097 (2018) . (22) Cardoso, M. J. et al. MONAI: An open-source framework for deep learning in healthcare (2022). ArXiv:2211.02701 [cs] . (23) Cardiff, R. D., Miller, C. H. & Munn, R. J. Manual Hematoxylin and Eosin Staining of Mouse Tissue Sections. Cold Spring Harbor Protocols 2014 (6), pdb.prot073411 (2014) . (24) Tomasovic, A. et al. Interference with ERK-dimerization at the nucleocytosolic interface targets pathological ERK1/2 signaling without cardiotoxic side-effects. Nature Communications 11 (1), 1733 (2020) . (25) Lorenz, K., Schmitt, J. P., Schmitteckert, E. M. & Lohse, M. J. A new type of ERK1/2 autophosphorylation causes cardiac hypertrophy. Nature Medicine 15 (1), 75–83 (2009) . (26) Grüneboom, A. et al. A network of trans-cortical capillaries as mainstay for blood circulation in long bones. Nature Metabolism 1 (2), 236–250 (2019) . (27) Ounkomol, C., Seshamani, S., Maleckar, M. M., Collman, F. & Johnson, G. R. Label-free prediction of three-dimensional fluorescence images from transmitted-light microscopy. Nature Methods 15 (11), 917–920 (2018) . (28) Viana, M. P. et al. Integrated intracellular organization and its variations in human iPS cells. Nature 613 (7943), 345–354 (2023) . Cardoso, M. J. et al. MONAI: An open-source framework for deep learning in healthcare (2022). ArXiv:2211.02701 [cs] . (23) Cardiff, R. D., Miller, C. H. & Munn, R. J. Manual Hematoxylin and Eosin Staining of Mouse Tissue Sections. Cold Spring Harbor Protocols 2014 (6), pdb.prot073411 (2014) . (24) Tomasovic, A. et al. Interference with ERK-dimerization at the nucleocytosolic interface targets pathological ERK1/2 signaling without cardiotoxic side-effects. Nature Communications 11 (1), 1733 (2020) . (25) Lorenz, K., Schmitt, J. P., Schmitteckert, E. M. & Lohse, M. J. A new type of ERK1/2 autophosphorylation causes cardiac hypertrophy. Nature Medicine 15 (1), 75–83 (2009) . (26) Grüneboom, A. et al. A network of trans-cortical capillaries as mainstay for blood circulation in long bones. Nature Metabolism 1 (2), 236–250 (2019) . (27) Ounkomol, C., Seshamani, S., Maleckar, M. M., Collman, F. & Johnson, G. R. Label-free prediction of three-dimensional fluorescence images from transmitted-light microscopy. Nature Methods 15 (11), 917–920 (2018) . (28) Viana, M. P. et al. Integrated intracellular organization and its variations in human iPS cells. Nature 613 (7943), 345–354 (2023) . Cardiff, R. D., Miller, C. H. & Munn, R. J. Manual Hematoxylin and Eosin Staining of Mouse Tissue Sections. Cold Spring Harbor Protocols 2014 (6), pdb.prot073411 (2014) . (24) Tomasovic, A. et al. Interference with ERK-dimerization at the nucleocytosolic interface targets pathological ERK1/2 signaling without cardiotoxic side-effects. Nature Communications 11 (1), 1733 (2020) . (25) Lorenz, K., Schmitt, J. P., Schmitteckert, E. M. & Lohse, M. J. A new type of ERK1/2 autophosphorylation causes cardiac hypertrophy. Nature Medicine 15 (1), 75–83 (2009) . (26) Grüneboom, A. et al. A network of trans-cortical capillaries as mainstay for blood circulation in long bones. Nature Metabolism 1 (2), 236–250 (2019) . (27) Ounkomol, C., Seshamani, S., Maleckar, M. M., Collman, F. & Johnson, G. R. Label-free prediction of three-dimensional fluorescence images from transmitted-light microscopy. Nature Methods 15 (11), 917–920 (2018) . (28) Viana, M. P. et al. Integrated intracellular organization and its variations in human iPS cells. Nature 613 (7943), 345–354 (2023) . Tomasovic, A. et al. Interference with ERK-dimerization at the nucleocytosolic interface targets pathological ERK1/2 signaling without cardiotoxic side-effects. Nature Communications 11 (1), 1733 (2020) . (25) Lorenz, K., Schmitt, J. P., Schmitteckert, E. M. & Lohse, M. J. A new type of ERK1/2 autophosphorylation causes cardiac hypertrophy. Nature Medicine 15 (1), 75–83 (2009) . (26) Grüneboom, A. et al. A network of trans-cortical capillaries as mainstay for blood circulation in long bones. Nature Metabolism 1 (2), 236–250 (2019) . (27) Ounkomol, C., Seshamani, S., Maleckar, M. M., Collman, F. & Johnson, G. R. Label-free prediction of three-dimensional fluorescence images from transmitted-light microscopy. Nature Methods 15 (11), 917–920 (2018) . (28) Viana, M. P. et al. Integrated intracellular organization and its variations in human iPS cells. Nature 613 (7943), 345–354 (2023) . Lorenz, K., Schmitt, J. P., Schmitteckert, E. M. & Lohse, M. J. A new type of ERK1/2 autophosphorylation causes cardiac hypertrophy. Nature Medicine 15 (1), 75–83 (2009) . (26) Grüneboom, A. et al. A network of trans-cortical capillaries as mainstay for blood circulation in long bones. Nature Metabolism 1 (2), 236–250 (2019) . (27) Ounkomol, C., Seshamani, S., Maleckar, M. M., Collman, F. & Johnson, G. R. Label-free prediction of three-dimensional fluorescence images from transmitted-light microscopy. Nature Methods 15 (11), 917–920 (2018) . (28) Viana, M. P. et al. Integrated intracellular organization and its variations in human iPS cells. Nature 613 (7943), 345–354 (2023) . Grüneboom, A. et al. A network of trans-cortical capillaries as mainstay for blood circulation in long bones. Nature Metabolism 1 (2), 236–250 (2019) . (27) Ounkomol, C., Seshamani, S., Maleckar, M. M., Collman, F. & Johnson, G. R. Label-free prediction of three-dimensional fluorescence images from transmitted-light microscopy. Nature Methods 15 (11), 917–920 (2018) . (28) Viana, M. P. et al. Integrated intracellular organization and its variations in human iPS cells. Nature 613 (7943), 345–354 (2023) . Ounkomol, C., Seshamani, S., Maleckar, M. M., Collman, F. & Johnson, G. R. Label-free prediction of three-dimensional fluorescence images from transmitted-light microscopy. Nature Methods 15 (11), 917–920 (2018) . (28) Viana, M. P. et al. Integrated intracellular organization and its variations in human iPS cells. Nature 613 (7943), 345–354 (2023) . Viana, M. P. et al. Integrated intracellular organization and its variations in human iPS cells. Nature 613 (7943), 345–354 (2023) .
  18. Micikevicius, P. et al. FP8 Formats for Deep Learning (2022). ArXiv:2209.05433 [cs]. (19) Han, S., Pool, J., Tran, J. & Dally, W. J. Learning both Weights and Connections for Efficient Neural Networks (2015). ArXiv:1506.02626 [cs] . (20) He, Y., Liu, P., Wang, Z., Hu, Z. & Yang, Y. Filter Pruning via Geometric Median for Deep Convolutional Neural Networks Acceleration (2018). ArXiv:1811.00250 [cs] . (21) Weigert, M. et al. Content-aware image restoration: pushing the limits of fluorescence microscopy. Nature Methods 15 (12), 1090–1097 (2018) . (22) Cardoso, M. J. et al. MONAI: An open-source framework for deep learning in healthcare (2022). ArXiv:2211.02701 [cs] . (23) Cardiff, R. D., Miller, C. H. & Munn, R. J. Manual Hematoxylin and Eosin Staining of Mouse Tissue Sections. Cold Spring Harbor Protocols 2014 (6), pdb.prot073411 (2014) . (24) Tomasovic, A. et al. Interference with ERK-dimerization at the nucleocytosolic interface targets pathological ERK1/2 signaling without cardiotoxic side-effects. Nature Communications 11 (1), 1733 (2020) . (25) Lorenz, K., Schmitt, J. P., Schmitteckert, E. M. & Lohse, M. J. A new type of ERK1/2 autophosphorylation causes cardiac hypertrophy. Nature Medicine 15 (1), 75–83 (2009) . (26) Grüneboom, A. et al. A network of trans-cortical capillaries as mainstay for blood circulation in long bones. Nature Metabolism 1 (2), 236–250 (2019) . (27) Ounkomol, C., Seshamani, S., Maleckar, M. M., Collman, F. & Johnson, G. R. Label-free prediction of three-dimensional fluorescence images from transmitted-light microscopy. Nature Methods 15 (11), 917–920 (2018) . (28) Viana, M. P. et al. Integrated intracellular organization and its variations in human iPS cells. Nature 613 (7943), 345–354 (2023) . Han, S., Pool, J., Tran, J. & Dally, W. J. Learning both Weights and Connections for Efficient Neural Networks (2015). ArXiv:1506.02626 [cs] . (20) He, Y., Liu, P., Wang, Z., Hu, Z. & Yang, Y. Filter Pruning via Geometric Median for Deep Convolutional Neural Networks Acceleration (2018). ArXiv:1811.00250 [cs] . (21) Weigert, M. et al. Content-aware image restoration: pushing the limits of fluorescence microscopy. Nature Methods 15 (12), 1090–1097 (2018) . (22) Cardoso, M. J. et al. MONAI: An open-source framework for deep learning in healthcare (2022). ArXiv:2211.02701 [cs] . (23) Cardiff, R. D., Miller, C. H. & Munn, R. J. Manual Hematoxylin and Eosin Staining of Mouse Tissue Sections. Cold Spring Harbor Protocols 2014 (6), pdb.prot073411 (2014) . (24) Tomasovic, A. et al. Interference with ERK-dimerization at the nucleocytosolic interface targets pathological ERK1/2 signaling without cardiotoxic side-effects. Nature Communications 11 (1), 1733 (2020) . (25) Lorenz, K., Schmitt, J. P., Schmitteckert, E. M. & Lohse, M. J. A new type of ERK1/2 autophosphorylation causes cardiac hypertrophy. Nature Medicine 15 (1), 75–83 (2009) . (26) Grüneboom, A. et al. A network of trans-cortical capillaries as mainstay for blood circulation in long bones. Nature Metabolism 1 (2), 236–250 (2019) . (27) Ounkomol, C., Seshamani, S., Maleckar, M. M., Collman, F. & Johnson, G. R. Label-free prediction of three-dimensional fluorescence images from transmitted-light microscopy. Nature Methods 15 (11), 917–920 (2018) . (28) Viana, M. P. et al. Integrated intracellular organization and its variations in human iPS cells. Nature 613 (7943), 345–354 (2023) . He, Y., Liu, P., Wang, Z., Hu, Z. & Yang, Y. Filter Pruning via Geometric Median for Deep Convolutional Neural Networks Acceleration (2018). ArXiv:1811.00250 [cs] . (21) Weigert, M. et al. Content-aware image restoration: pushing the limits of fluorescence microscopy. Nature Methods 15 (12), 1090–1097 (2018) . (22) Cardoso, M. J. et al. MONAI: An open-source framework for deep learning in healthcare (2022). ArXiv:2211.02701 [cs] . (23) Cardiff, R. D., Miller, C. H. & Munn, R. J. Manual Hematoxylin and Eosin Staining of Mouse Tissue Sections. Cold Spring Harbor Protocols 2014 (6), pdb.prot073411 (2014) . (24) Tomasovic, A. et al. Interference with ERK-dimerization at the nucleocytosolic interface targets pathological ERK1/2 signaling without cardiotoxic side-effects. Nature Communications 11 (1), 1733 (2020) . (25) Lorenz, K., Schmitt, J. P., Schmitteckert, E. M. & Lohse, M. J. A new type of ERK1/2 autophosphorylation causes cardiac hypertrophy. Nature Medicine 15 (1), 75–83 (2009) . (26) Grüneboom, A. et al. A network of trans-cortical capillaries as mainstay for blood circulation in long bones. Nature Metabolism 1 (2), 236–250 (2019) . (27) Ounkomol, C., Seshamani, S., Maleckar, M. M., Collman, F. & Johnson, G. R. Label-free prediction of three-dimensional fluorescence images from transmitted-light microscopy. Nature Methods 15 (11), 917–920 (2018) . (28) Viana, M. P. et al. Integrated intracellular organization and its variations in human iPS cells. Nature 613 (7943), 345–354 (2023) . Weigert, M. et al. Content-aware image restoration: pushing the limits of fluorescence microscopy. Nature Methods 15 (12), 1090–1097 (2018) . (22) Cardoso, M. J. et al. MONAI: An open-source framework for deep learning in healthcare (2022). ArXiv:2211.02701 [cs] . (23) Cardiff, R. D., Miller, C. H. & Munn, R. J. Manual Hematoxylin and Eosin Staining of Mouse Tissue Sections. Cold Spring Harbor Protocols 2014 (6), pdb.prot073411 (2014) . (24) Tomasovic, A. et al. Interference with ERK-dimerization at the nucleocytosolic interface targets pathological ERK1/2 signaling without cardiotoxic side-effects. Nature Communications 11 (1), 1733 (2020) . (25) Lorenz, K., Schmitt, J. P., Schmitteckert, E. M. & Lohse, M. J. A new type of ERK1/2 autophosphorylation causes cardiac hypertrophy. Nature Medicine 15 (1), 75–83 (2009) . (26) Grüneboom, A. et al. A network of trans-cortical capillaries as mainstay for blood circulation in long bones. Nature Metabolism 1 (2), 236–250 (2019) . (27) Ounkomol, C., Seshamani, S., Maleckar, M. M., Collman, F. & Johnson, G. R. Label-free prediction of three-dimensional fluorescence images from transmitted-light microscopy. Nature Methods 15 (11), 917–920 (2018) . (28) Viana, M. P. et al. Integrated intracellular organization and its variations in human iPS cells. Nature 613 (7943), 345–354 (2023) . Cardoso, M. J. et al. MONAI: An open-source framework for deep learning in healthcare (2022). ArXiv:2211.02701 [cs] . (23) Cardiff, R. D., Miller, C. H. & Munn, R. J. Manual Hematoxylin and Eosin Staining of Mouse Tissue Sections. Cold Spring Harbor Protocols 2014 (6), pdb.prot073411 (2014) . (24) Tomasovic, A. et al. Interference with ERK-dimerization at the nucleocytosolic interface targets pathological ERK1/2 signaling without cardiotoxic side-effects. Nature Communications 11 (1), 1733 (2020) . (25) Lorenz, K., Schmitt, J. P., Schmitteckert, E. M. & Lohse, M. J. A new type of ERK1/2 autophosphorylation causes cardiac hypertrophy. Nature Medicine 15 (1), 75–83 (2009) . (26) Grüneboom, A. et al. A network of trans-cortical capillaries as mainstay for blood circulation in long bones. Nature Metabolism 1 (2), 236–250 (2019) . (27) Ounkomol, C., Seshamani, S., Maleckar, M. M., Collman, F. & Johnson, G. R. Label-free prediction of three-dimensional fluorescence images from transmitted-light microscopy. Nature Methods 15 (11), 917–920 (2018) . (28) Viana, M. P. et al. Integrated intracellular organization and its variations in human iPS cells. Nature 613 (7943), 345–354 (2023) . Cardiff, R. D., Miller, C. H. & Munn, R. J. Manual Hematoxylin and Eosin Staining of Mouse Tissue Sections. Cold Spring Harbor Protocols 2014 (6), pdb.prot073411 (2014) . (24) Tomasovic, A. et al. Interference with ERK-dimerization at the nucleocytosolic interface targets pathological ERK1/2 signaling without cardiotoxic side-effects. Nature Communications 11 (1), 1733 (2020) . (25) Lorenz, K., Schmitt, J. P., Schmitteckert, E. M. & Lohse, M. J. A new type of ERK1/2 autophosphorylation causes cardiac hypertrophy. Nature Medicine 15 (1), 75–83 (2009) . (26) Grüneboom, A. et al. A network of trans-cortical capillaries as mainstay for blood circulation in long bones. Nature Metabolism 1 (2), 236–250 (2019) . (27) Ounkomol, C., Seshamani, S., Maleckar, M. M., Collman, F. & Johnson, G. R. Label-free prediction of three-dimensional fluorescence images from transmitted-light microscopy. Nature Methods 15 (11), 917–920 (2018) . (28) Viana, M. P. et al. Integrated intracellular organization and its variations in human iPS cells. Nature 613 (7943), 345–354 (2023) . Tomasovic, A. et al. Interference with ERK-dimerization at the nucleocytosolic interface targets pathological ERK1/2 signaling without cardiotoxic side-effects. Nature Communications 11 (1), 1733 (2020) . (25) Lorenz, K., Schmitt, J. P., Schmitteckert, E. M. & Lohse, M. J. A new type of ERK1/2 autophosphorylation causes cardiac hypertrophy. Nature Medicine 15 (1), 75–83 (2009) . (26) Grüneboom, A. et al. A network of trans-cortical capillaries as mainstay for blood circulation in long bones. Nature Metabolism 1 (2), 236–250 (2019) . (27) Ounkomol, C., Seshamani, S., Maleckar, M. M., Collman, F. & Johnson, G. R. Label-free prediction of three-dimensional fluorescence images from transmitted-light microscopy. Nature Methods 15 (11), 917–920 (2018) . (28) Viana, M. P. et al. Integrated intracellular organization and its variations in human iPS cells. Nature 613 (7943), 345–354 (2023) . Lorenz, K., Schmitt, J. P., Schmitteckert, E. M. & Lohse, M. J. A new type of ERK1/2 autophosphorylation causes cardiac hypertrophy. Nature Medicine 15 (1), 75–83 (2009) . (26) Grüneboom, A. et al. A network of trans-cortical capillaries as mainstay for blood circulation in long bones. Nature Metabolism 1 (2), 236–250 (2019) . (27) Ounkomol, C., Seshamani, S., Maleckar, M. M., Collman, F. & Johnson, G. R. Label-free prediction of three-dimensional fluorescence images from transmitted-light microscopy. Nature Methods 15 (11), 917–920 (2018) . (28) Viana, M. P. et al. Integrated intracellular organization and its variations in human iPS cells. Nature 613 (7943), 345–354 (2023) . Grüneboom, A. et al. A network of trans-cortical capillaries as mainstay for blood circulation in long bones. Nature Metabolism 1 (2), 236–250 (2019) . (27) Ounkomol, C., Seshamani, S., Maleckar, M. M., Collman, F. & Johnson, G. R. Label-free prediction of three-dimensional fluorescence images from transmitted-light microscopy. Nature Methods 15 (11), 917–920 (2018) . (28) Viana, M. P. et al. Integrated intracellular organization and its variations in human iPS cells. Nature 613 (7943), 345–354 (2023) . Ounkomol, C., Seshamani, S., Maleckar, M. M., Collman, F. & Johnson, G. R. Label-free prediction of three-dimensional fluorescence images from transmitted-light microscopy. Nature Methods 15 (11), 917–920 (2018) . (28) Viana, M. P. et al. Integrated intracellular organization and its variations in human iPS cells. Nature 613 (7943), 345–354 (2023) . Viana, M. P. et al. Integrated intracellular organization and its variations in human iPS cells. Nature 613 (7943), 345–354 (2023) .
  19. Learning both Weights and Connections for Efficient Neural Networks (2015). ArXiv:1506.02626 [cs] . (20) He, Y., Liu, P., Wang, Z., Hu, Z. & Yang, Y. Filter Pruning via Geometric Median for Deep Convolutional Neural Networks Acceleration (2018). ArXiv:1811.00250 [cs] . (21) Weigert, M. et al. Content-aware image restoration: pushing the limits of fluorescence microscopy. Nature Methods 15 (12), 1090–1097 (2018) . (22) Cardoso, M. J. et al. MONAI: An open-source framework for deep learning in healthcare (2022). ArXiv:2211.02701 [cs] . (23) Cardiff, R. D., Miller, C. H. & Munn, R. J. Manual Hematoxylin and Eosin Staining of Mouse Tissue Sections. Cold Spring Harbor Protocols 2014 (6), pdb.prot073411 (2014) . (24) Tomasovic, A. et al. Interference with ERK-dimerization at the nucleocytosolic interface targets pathological ERK1/2 signaling without cardiotoxic side-effects. Nature Communications 11 (1), 1733 (2020) . (25) Lorenz, K., Schmitt, J. P., Schmitteckert, E. M. & Lohse, M. J. A new type of ERK1/2 autophosphorylation causes cardiac hypertrophy. Nature Medicine 15 (1), 75–83 (2009) . (26) Grüneboom, A. et al. A network of trans-cortical capillaries as mainstay for blood circulation in long bones. Nature Metabolism 1 (2), 236–250 (2019) . (27) Ounkomol, C., Seshamani, S., Maleckar, M. M., Collman, F. & Johnson, G. R. Label-free prediction of three-dimensional fluorescence images from transmitted-light microscopy. Nature Methods 15 (11), 917–920 (2018) . (28) Viana, M. P. et al. Integrated intracellular organization and its variations in human iPS cells. Nature 613 (7943), 345–354 (2023) . He, Y., Liu, P., Wang, Z., Hu, Z. & Yang, Y. Filter Pruning via Geometric Median for Deep Convolutional Neural Networks Acceleration (2018). ArXiv:1811.00250 [cs] . (21) Weigert, M. et al. Content-aware image restoration: pushing the limits of fluorescence microscopy. 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Interference with ERK-dimerization at the nucleocytosolic interface targets pathological ERK1/2 signaling without cardiotoxic side-effects. Nature Communications 11 (1), 1733 (2020) . (25) Lorenz, K., Schmitt, J. P., Schmitteckert, E. M. & Lohse, M. J. A new type of ERK1/2 autophosphorylation causes cardiac hypertrophy. Nature Medicine 15 (1), 75–83 (2009) . (26) Grüneboom, A. et al. A network of trans-cortical capillaries as mainstay for blood circulation in long bones. Nature Metabolism 1 (2), 236–250 (2019) . (27) Ounkomol, C., Seshamani, S., Maleckar, M. M., Collman, F. & Johnson, G. R. Label-free prediction of three-dimensional fluorescence images from transmitted-light microscopy. Nature Methods 15 (11), 917–920 (2018) . (28) Viana, M. P. et al. Integrated intracellular organization and its variations in human iPS cells. Nature 613 (7943), 345–354 (2023) . Cardiff, R. D., Miller, C. H. & Munn, R. J. Manual Hematoxylin and Eosin Staining of Mouse Tissue Sections. Cold Spring Harbor Protocols 2014 (6), pdb.prot073411 (2014) . (24) Tomasovic, A. et al. Interference with ERK-dimerization at the nucleocytosolic interface targets pathological ERK1/2 signaling without cardiotoxic side-effects. Nature Communications 11 (1), 1733 (2020) . (25) Lorenz, K., Schmitt, J. P., Schmitteckert, E. M. & Lohse, M. J. A new type of ERK1/2 autophosphorylation causes cardiac hypertrophy. Nature Medicine 15 (1), 75–83 (2009) . (26) Grüneboom, A. et al. A network of trans-cortical capillaries as mainstay for blood circulation in long bones. Nature Metabolism 1 (2), 236–250 (2019) . (27) Ounkomol, C., Seshamani, S., Maleckar, M. M., Collman, F. & Johnson, G. R. Label-free prediction of three-dimensional fluorescence images from transmitted-light microscopy. Nature Methods 15 (11), 917–920 (2018) . (28) Viana, M. P. et al. Integrated intracellular organization and its variations in human iPS cells. Nature 613 (7943), 345–354 (2023) . Tomasovic, A. et al. Interference with ERK-dimerization at the nucleocytosolic interface targets pathological ERK1/2 signaling without cardiotoxic side-effects. Nature Communications 11 (1), 1733 (2020) . (25) Lorenz, K., Schmitt, J. P., Schmitteckert, E. M. & Lohse, M. J. A new type of ERK1/2 autophosphorylation causes cardiac hypertrophy. Nature Medicine 15 (1), 75–83 (2009) . (26) Grüneboom, A. et al. A network of trans-cortical capillaries as mainstay for blood circulation in long bones. Nature Metabolism 1 (2), 236–250 (2019) . (27) Ounkomol, C., Seshamani, S., Maleckar, M. M., Collman, F. & Johnson, G. R. Label-free prediction of three-dimensional fluorescence images from transmitted-light microscopy. Nature Methods 15 (11), 917–920 (2018) . (28) Viana, M. P. et al. Integrated intracellular organization and its variations in human iPS cells. Nature 613 (7943), 345–354 (2023) . Lorenz, K., Schmitt, J. P., Schmitteckert, E. M. & Lohse, M. J. A new type of ERK1/2 autophosphorylation causes cardiac hypertrophy. Nature Medicine 15 (1), 75–83 (2009) . (26) Grüneboom, A. et al. A network of trans-cortical capillaries as mainstay for blood circulation in long bones. Nature Metabolism 1 (2), 236–250 (2019) . (27) Ounkomol, C., Seshamani, S., Maleckar, M. M., Collman, F. & Johnson, G. R. Label-free prediction of three-dimensional fluorescence images from transmitted-light microscopy. Nature Methods 15 (11), 917–920 (2018) . (28) Viana, M. P. et al. Integrated intracellular organization and its variations in human iPS cells. Nature 613 (7943), 345–354 (2023) . Grüneboom, A. et al. A network of trans-cortical capillaries as mainstay for blood circulation in long bones. Nature Metabolism 1 (2), 236–250 (2019) . (27) Ounkomol, C., Seshamani, S., Maleckar, M. M., Collman, F. & Johnson, G. R. Label-free prediction of three-dimensional fluorescence images from transmitted-light microscopy. Nature Methods 15 (11), 917–920 (2018) . (28) Viana, M. P. et al. Integrated intracellular organization and its variations in human iPS cells. Nature 613 (7943), 345–354 (2023) . Ounkomol, C., Seshamani, S., Maleckar, M. M., Collman, F. & Johnson, G. R. Label-free prediction of three-dimensional fluorescence images from transmitted-light microscopy. Nature Methods 15 (11), 917–920 (2018) . (28) Viana, M. P. et al. Integrated intracellular organization and its variations in human iPS cells. Nature 613 (7943), 345–354 (2023) . Viana, M. P. et al. Integrated intracellular organization and its variations in human iPS cells. Nature 613 (7943), 345–354 (2023) .
  20. Filter Pruning via Geometric Median for Deep Convolutional Neural Networks Acceleration (2018). ArXiv:1811.00250 [cs] . (21) Weigert, M. et al. Content-aware image restoration: pushing the limits of fluorescence microscopy. Nature Methods 15 (12), 1090–1097 (2018) . (22) Cardoso, M. J. et al. MONAI: An open-source framework for deep learning in healthcare (2022). ArXiv:2211.02701 [cs] . (23) Cardiff, R. D., Miller, C. H. & Munn, R. J. Manual Hematoxylin and Eosin Staining of Mouse Tissue Sections. Cold Spring Harbor Protocols 2014 (6), pdb.prot073411 (2014) . (24) Tomasovic, A. et al. Interference with ERK-dimerization at the nucleocytosolic interface targets pathological ERK1/2 signaling without cardiotoxic side-effects. Nature Communications 11 (1), 1733 (2020) . (25) Lorenz, K., Schmitt, J. P., Schmitteckert, E. M. & Lohse, M. J. A new type of ERK1/2 autophosphorylation causes cardiac hypertrophy. Nature Medicine 15 (1), 75–83 (2009) . (26) Grüneboom, A. et al. A network of trans-cortical capillaries as mainstay for blood circulation in long bones. Nature Metabolism 1 (2), 236–250 (2019) . (27) Ounkomol, C., Seshamani, S., Maleckar, M. M., Collman, F. & Johnson, G. R. Label-free prediction of three-dimensional fluorescence images from transmitted-light microscopy. Nature Methods 15 (11), 917–920 (2018) . (28) Viana, M. P. et al. Integrated intracellular organization and its variations in human iPS cells. Nature 613 (7943), 345–354 (2023) . Weigert, M. et al. Content-aware image restoration: pushing the limits of fluorescence microscopy. Nature Methods 15 (12), 1090–1097 (2018) . (22) Cardoso, M. J. et al. MONAI: An open-source framework for deep learning in healthcare (2022). ArXiv:2211.02701 [cs] . (23) Cardiff, R. D., Miller, C. H. & Munn, R. J. Manual Hematoxylin and Eosin Staining of Mouse Tissue Sections. Cold Spring Harbor Protocols 2014 (6), pdb.prot073411 (2014) . (24) Tomasovic, A. et al. Interference with ERK-dimerization at the nucleocytosolic interface targets pathological ERK1/2 signaling without cardiotoxic side-effects. Nature Communications 11 (1), 1733 (2020) . (25) Lorenz, K., Schmitt, J. P., Schmitteckert, E. M. & Lohse, M. J. A new type of ERK1/2 autophosphorylation causes cardiac hypertrophy. Nature Medicine 15 (1), 75–83 (2009) . (26) Grüneboom, A. et al. A network of trans-cortical capillaries as mainstay for blood circulation in long bones. Nature Metabolism 1 (2), 236–250 (2019) . (27) Ounkomol, C., Seshamani, S., Maleckar, M. M., Collman, F. & Johnson, G. R. Label-free prediction of three-dimensional fluorescence images from transmitted-light microscopy. Nature Methods 15 (11), 917–920 (2018) . (28) Viana, M. P. et al. Integrated intracellular organization and its variations in human iPS cells. Nature 613 (7943), 345–354 (2023) . Cardoso, M. J. et al. MONAI: An open-source framework for deep learning in healthcare (2022). ArXiv:2211.02701 [cs] . (23) Cardiff, R. D., Miller, C. H. & Munn, R. J. Manual Hematoxylin and Eosin Staining of Mouse Tissue Sections. Cold Spring Harbor Protocols 2014 (6), pdb.prot073411 (2014) . (24) Tomasovic, A. et al. Interference with ERK-dimerization at the nucleocytosolic interface targets pathological ERK1/2 signaling without cardiotoxic side-effects. Nature Communications 11 (1), 1733 (2020) . (25) Lorenz, K., Schmitt, J. P., Schmitteckert, E. M. & Lohse, M. J. A new type of ERK1/2 autophosphorylation causes cardiac hypertrophy. Nature Medicine 15 (1), 75–83 (2009) . (26) Grüneboom, A. et al. A network of trans-cortical capillaries as mainstay for blood circulation in long bones. Nature Metabolism 1 (2), 236–250 (2019) . (27) Ounkomol, C., Seshamani, S., Maleckar, M. M., Collman, F. & Johnson, G. R. Label-free prediction of three-dimensional fluorescence images from transmitted-light microscopy. Nature Methods 15 (11), 917–920 (2018) . (28) Viana, M. P. et al. Integrated intracellular organization and its variations in human iPS cells. Nature 613 (7943), 345–354 (2023) . Cardiff, R. D., Miller, C. H. & Munn, R. J. Manual Hematoxylin and Eosin Staining of Mouse Tissue Sections. Cold Spring Harbor Protocols 2014 (6), pdb.prot073411 (2014) . (24) Tomasovic, A. et al. Interference with ERK-dimerization at the nucleocytosolic interface targets pathological ERK1/2 signaling without cardiotoxic side-effects. Nature Communications 11 (1), 1733 (2020) . (25) Lorenz, K., Schmitt, J. P., Schmitteckert, E. M. & Lohse, M. J. A new type of ERK1/2 autophosphorylation causes cardiac hypertrophy. Nature Medicine 15 (1), 75–83 (2009) . (26) Grüneboom, A. et al. A network of trans-cortical capillaries as mainstay for blood circulation in long bones. Nature Metabolism 1 (2), 236–250 (2019) . (27) Ounkomol, C., Seshamani, S., Maleckar, M. M., Collman, F. & Johnson, G. R. Label-free prediction of three-dimensional fluorescence images from transmitted-light microscopy. Nature Methods 15 (11), 917–920 (2018) . (28) Viana, M. P. et al. Integrated intracellular organization and its variations in human iPS cells. Nature 613 (7943), 345–354 (2023) . Tomasovic, A. et al. Interference with ERK-dimerization at the nucleocytosolic interface targets pathological ERK1/2 signaling without cardiotoxic side-effects. Nature Communications 11 (1), 1733 (2020) . (25) Lorenz, K., Schmitt, J. P., Schmitteckert, E. M. & Lohse, M. J. A new type of ERK1/2 autophosphorylation causes cardiac hypertrophy. Nature Medicine 15 (1), 75–83 (2009) . (26) Grüneboom, A. et al. A network of trans-cortical capillaries as mainstay for blood circulation in long bones. Nature Metabolism 1 (2), 236–250 (2019) . (27) Ounkomol, C., Seshamani, S., Maleckar, M. M., Collman, F. & Johnson, G. R. Label-free prediction of three-dimensional fluorescence images from transmitted-light microscopy. Nature Methods 15 (11), 917–920 (2018) . (28) Viana, M. P. et al. Integrated intracellular organization and its variations in human iPS cells. Nature 613 (7943), 345–354 (2023) . Lorenz, K., Schmitt, J. P., Schmitteckert, E. M. & Lohse, M. J. A new type of ERK1/2 autophosphorylation causes cardiac hypertrophy. Nature Medicine 15 (1), 75–83 (2009) . (26) Grüneboom, A. et al. A network of trans-cortical capillaries as mainstay for blood circulation in long bones. Nature Metabolism 1 (2), 236–250 (2019) . (27) Ounkomol, C., Seshamani, S., Maleckar, M. M., Collman, F. & Johnson, G. R. Label-free prediction of three-dimensional fluorescence images from transmitted-light microscopy. Nature Methods 15 (11), 917–920 (2018) . (28) Viana, M. P. et al. Integrated intracellular organization and its variations in human iPS cells. Nature 613 (7943), 345–354 (2023) . Grüneboom, A. et al. A network of trans-cortical capillaries as mainstay for blood circulation in long bones. Nature Metabolism 1 (2), 236–250 (2019) . (27) Ounkomol, C., Seshamani, S., Maleckar, M. M., Collman, F. & Johnson, G. R. Label-free prediction of three-dimensional fluorescence images from transmitted-light microscopy. Nature Methods 15 (11), 917–920 (2018) . (28) Viana, M. P. et al. Integrated intracellular organization and its variations in human iPS cells. Nature 613 (7943), 345–354 (2023) . Ounkomol, C., Seshamani, S., Maleckar, M. M., Collman, F. & Johnson, G. R. Label-free prediction of three-dimensional fluorescence images from transmitted-light microscopy. Nature Methods 15 (11), 917–920 (2018) . (28) Viana, M. P. et al. Integrated intracellular organization and its variations in human iPS cells. Nature 613 (7943), 345–354 (2023) . Viana, M. P. et al. Integrated intracellular organization and its variations in human iPS cells. Nature 613 (7943), 345–354 (2023) .
  21. Weigert, M. et al. Content-aware image restoration: pushing the limits of fluorescence microscopy. Nature Methods 15 (12), 1090–1097 (2018) . (22) Cardoso, M. J. et al. MONAI: An open-source framework for deep learning in healthcare (2022). ArXiv:2211.02701 [cs] . (23) Cardiff, R. D., Miller, C. H. & Munn, R. J. Manual Hematoxylin and Eosin Staining of Mouse Tissue Sections. Cold Spring Harbor Protocols 2014 (6), pdb.prot073411 (2014) . (24) Tomasovic, A. et al. Interference with ERK-dimerization at the nucleocytosolic interface targets pathological ERK1/2 signaling without cardiotoxic side-effects. Nature Communications 11 (1), 1733 (2020) . (25) Lorenz, K., Schmitt, J. P., Schmitteckert, E. M. & Lohse, M. J. A new type of ERK1/2 autophosphorylation causes cardiac hypertrophy. Nature Medicine 15 (1), 75–83 (2009) . (26) Grüneboom, A. et al. A network of trans-cortical capillaries as mainstay for blood circulation in long bones. Nature Metabolism 1 (2), 236–250 (2019) . (27) Ounkomol, C., Seshamani, S., Maleckar, M. M., Collman, F. & Johnson, G. R. Label-free prediction of three-dimensional fluorescence images from transmitted-light microscopy. Nature Methods 15 (11), 917–920 (2018) . (28) Viana, M. P. et al. Integrated intracellular organization and its variations in human iPS cells. Nature 613 (7943), 345–354 (2023) . Cardoso, M. J. et al. MONAI: An open-source framework for deep learning in healthcare (2022). ArXiv:2211.02701 [cs] . (23) Cardiff, R. D., Miller, C. H. & Munn, R. J. Manual Hematoxylin and Eosin Staining of Mouse Tissue Sections. Cold Spring Harbor Protocols 2014 (6), pdb.prot073411 (2014) . (24) Tomasovic, A. et al. Interference with ERK-dimerization at the nucleocytosolic interface targets pathological ERK1/2 signaling without cardiotoxic side-effects. Nature Communications 11 (1), 1733 (2020) . (25) Lorenz, K., Schmitt, J. P., Schmitteckert, E. M. & Lohse, M. J. A new type of ERK1/2 autophosphorylation causes cardiac hypertrophy. Nature Medicine 15 (1), 75–83 (2009) . (26) Grüneboom, A. et al. A network of trans-cortical capillaries as mainstay for blood circulation in long bones. Nature Metabolism 1 (2), 236–250 (2019) . (27) Ounkomol, C., Seshamani, S., Maleckar, M. M., Collman, F. & Johnson, G. R. Label-free prediction of three-dimensional fluorescence images from transmitted-light microscopy. Nature Methods 15 (11), 917–920 (2018) . (28) Viana, M. P. et al. Integrated intracellular organization and its variations in human iPS cells. Nature 613 (7943), 345–354 (2023) . Cardiff, R. D., Miller, C. H. & Munn, R. J. Manual Hematoxylin and Eosin Staining of Mouse Tissue Sections. Cold Spring Harbor Protocols 2014 (6), pdb.prot073411 (2014) . (24) Tomasovic, A. et al. Interference with ERK-dimerization at the nucleocytosolic interface targets pathological ERK1/2 signaling without cardiotoxic side-effects. Nature Communications 11 (1), 1733 (2020) . (25) Lorenz, K., Schmitt, J. P., Schmitteckert, E. M. & Lohse, M. J. A new type of ERK1/2 autophosphorylation causes cardiac hypertrophy. Nature Medicine 15 (1), 75–83 (2009) . (26) Grüneboom, A. et al. A network of trans-cortical capillaries as mainstay for blood circulation in long bones. Nature Metabolism 1 (2), 236–250 (2019) . (27) Ounkomol, C., Seshamani, S., Maleckar, M. M., Collman, F. & Johnson, G. R. Label-free prediction of three-dimensional fluorescence images from transmitted-light microscopy. Nature Methods 15 (11), 917–920 (2018) . (28) Viana, M. P. et al. Integrated intracellular organization and its variations in human iPS cells. Nature 613 (7943), 345–354 (2023) . Tomasovic, A. et al. Interference with ERK-dimerization at the nucleocytosolic interface targets pathological ERK1/2 signaling without cardiotoxic side-effects. Nature Communications 11 (1), 1733 (2020) . (25) Lorenz, K., Schmitt, J. P., Schmitteckert, E. M. & Lohse, M. J. A new type of ERK1/2 autophosphorylation causes cardiac hypertrophy. Nature Medicine 15 (1), 75–83 (2009) . (26) Grüneboom, A. et al. A network of trans-cortical capillaries as mainstay for blood circulation in long bones. Nature Metabolism 1 (2), 236–250 (2019) . (27) Ounkomol, C., Seshamani, S., Maleckar, M. M., Collman, F. & Johnson, G. R. Label-free prediction of three-dimensional fluorescence images from transmitted-light microscopy. Nature Methods 15 (11), 917–920 (2018) . (28) Viana, M. P. et al. Integrated intracellular organization and its variations in human iPS cells. Nature 613 (7943), 345–354 (2023) . Lorenz, K., Schmitt, J. P., Schmitteckert, E. M. & Lohse, M. J. A new type of ERK1/2 autophosphorylation causes cardiac hypertrophy. Nature Medicine 15 (1), 75–83 (2009) . (26) Grüneboom, A. et al. A network of trans-cortical capillaries as mainstay for blood circulation in long bones. Nature Metabolism 1 (2), 236–250 (2019) . (27) Ounkomol, C., Seshamani, S., Maleckar, M. M., Collman, F. & Johnson, G. R. Label-free prediction of three-dimensional fluorescence images from transmitted-light microscopy. Nature Methods 15 (11), 917–920 (2018) . (28) Viana, M. P. et al. Integrated intracellular organization and its variations in human iPS cells. Nature 613 (7943), 345–354 (2023) . Grüneboom, A. et al. A network of trans-cortical capillaries as mainstay for blood circulation in long bones. Nature Metabolism 1 (2), 236–250 (2019) . (27) Ounkomol, C., Seshamani, S., Maleckar, M. M., Collman, F. & Johnson, G. R. Label-free prediction of three-dimensional fluorescence images from transmitted-light microscopy. Nature Methods 15 (11), 917–920 (2018) . (28) Viana, M. P. et al. Integrated intracellular organization and its variations in human iPS cells. Nature 613 (7943), 345–354 (2023) . Ounkomol, C., Seshamani, S., Maleckar, M. M., Collman, F. & Johnson, G. R. Label-free prediction of three-dimensional fluorescence images from transmitted-light microscopy. Nature Methods 15 (11), 917–920 (2018) . (28) Viana, M. P. et al. Integrated intracellular organization and its variations in human iPS cells. Nature 613 (7943), 345–354 (2023) . Viana, M. P. et al. Integrated intracellular organization and its variations in human iPS cells. Nature 613 (7943), 345–354 (2023) .
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  24. Tomasovic, A. et al. Interference with ERK-dimerization at the nucleocytosolic interface targets pathological ERK1/2 signaling without cardiotoxic side-effects. Nature Communications 11 (1), 1733 (2020) . (25) Lorenz, K., Schmitt, J. P., Schmitteckert, E. M. & Lohse, M. J. A new type of ERK1/2 autophosphorylation causes cardiac hypertrophy. Nature Medicine 15 (1), 75–83 (2009) . (26) Grüneboom, A. et al. A network of trans-cortical capillaries as mainstay for blood circulation in long bones. Nature Metabolism 1 (2), 236–250 (2019) . (27) Ounkomol, C., Seshamani, S., Maleckar, M. M., Collman, F. & Johnson, G. R. Label-free prediction of three-dimensional fluorescence images from transmitted-light microscopy. Nature Methods 15 (11), 917–920 (2018) . (28) Viana, M. P. et al. Integrated intracellular organization and its variations in human iPS cells. Nature 613 (7943), 345–354 (2023) . Lorenz, K., Schmitt, J. P., Schmitteckert, E. M. & Lohse, M. J. A new type of ERK1/2 autophosphorylation causes cardiac hypertrophy. Nature Medicine 15 (1), 75–83 (2009) . (26) Grüneboom, A. et al. A network of trans-cortical capillaries as mainstay for blood circulation in long bones. Nature Metabolism 1 (2), 236–250 (2019) . (27) Ounkomol, C., Seshamani, S., Maleckar, M. M., Collman, F. & Johnson, G. R. Label-free prediction of three-dimensional fluorescence images from transmitted-light microscopy. Nature Methods 15 (11), 917–920 (2018) . (28) Viana, M. P. et al. Integrated intracellular organization and its variations in human iPS cells. Nature 613 (7943), 345–354 (2023) . Grüneboom, A. et al. A network of trans-cortical capillaries as mainstay for blood circulation in long bones. Nature Metabolism 1 (2), 236–250 (2019) . (27) Ounkomol, C., Seshamani, S., Maleckar, M. M., Collman, F. & Johnson, G. R. Label-free prediction of three-dimensional fluorescence images from transmitted-light microscopy. Nature Methods 15 (11), 917–920 (2018) . (28) Viana, M. P. et al. Integrated intracellular organization and its variations in human iPS cells. Nature 613 (7943), 345–354 (2023) . Ounkomol, C., Seshamani, S., Maleckar, M. M., Collman, F. & Johnson, G. R. Label-free prediction of three-dimensional fluorescence images from transmitted-light microscopy. Nature Methods 15 (11), 917–920 (2018) . (28) Viana, M. P. et al. Integrated intracellular organization and its variations in human iPS cells. Nature 613 (7943), 345–354 (2023) . Viana, M. P. et al. Integrated intracellular organization and its variations in human iPS cells. Nature 613 (7943), 345–354 (2023) .
  25. A new type of ERK1/2 autophosphorylation causes cardiac hypertrophy. Nature Medicine 15 (1), 75–83 (2009) . (26) Grüneboom, A. et al. A network of trans-cortical capillaries as mainstay for blood circulation in long bones. Nature Metabolism 1 (2), 236–250 (2019) . (27) Ounkomol, C., Seshamani, S., Maleckar, M. M., Collman, F. & Johnson, G. R. Label-free prediction of three-dimensional fluorescence images from transmitted-light microscopy. Nature Methods 15 (11), 917–920 (2018) . (28) Viana, M. P. et al. Integrated intracellular organization and its variations in human iPS cells. Nature 613 (7943), 345–354 (2023) . Grüneboom, A. et al. A network of trans-cortical capillaries as mainstay for blood circulation in long bones. Nature Metabolism 1 (2), 236–250 (2019) . (27) Ounkomol, C., Seshamani, S., Maleckar, M. M., Collman, F. & Johnson, G. R. Label-free prediction of three-dimensional fluorescence images from transmitted-light microscopy. Nature Methods 15 (11), 917–920 (2018) . (28) Viana, M. P. et al. Integrated intracellular organization and its variations in human iPS cells. Nature 613 (7943), 345–354 (2023) . Ounkomol, C., Seshamani, S., Maleckar, M. M., Collman, F. & Johnson, G. R. Label-free prediction of three-dimensional fluorescence images from transmitted-light microscopy. Nature Methods 15 (11), 917–920 (2018) . (28) Viana, M. P. et al. Integrated intracellular organization and its variations in human iPS cells. Nature 613 (7943), 345–354 (2023) . Viana, M. P. et al. Integrated intracellular organization and its variations in human iPS cells. Nature 613 (7943), 345–354 (2023) .
  26. Grüneboom, A. et al. A network of trans-cortical capillaries as mainstay for blood circulation in long bones. Nature Metabolism 1 (2), 236–250 (2019) . (27) Ounkomol, C., Seshamani, S., Maleckar, M. M., Collman, F. & Johnson, G. R. Label-free prediction of three-dimensional fluorescence images from transmitted-light microscopy. Nature Methods 15 (11), 917–920 (2018) . (28) Viana, M. P. et al. Integrated intracellular organization and its variations in human iPS cells. Nature 613 (7943), 345–354 (2023) . Ounkomol, C., Seshamani, S., Maleckar, M. M., Collman, F. & Johnson, G. R. Label-free prediction of three-dimensional fluorescence images from transmitted-light microscopy. Nature Methods 15 (11), 917–920 (2018) . (28) Viana, M. P. et al. Integrated intracellular organization and its variations in human iPS cells. Nature 613 (7943), 345–354 (2023) . Viana, M. P. et al. Integrated intracellular organization and its variations in human iPS cells. Nature 613 (7943), 345–354 (2023) .
  27. Label-free prediction of three-dimensional fluorescence images from transmitted-light microscopy. Nature Methods 15 (11), 917–920 (2018) . (28) Viana, M. P. et al. Integrated intracellular organization and its variations in human iPS cells. Nature 613 (7943), 345–354 (2023) . Viana, M. P. et al. Integrated intracellular organization and its variations in human iPS cells. Nature 613 (7943), 345–354 (2023) .
  28. Viana, M. P. et al. Integrated intracellular organization and its variations in human iPS cells. Nature 613 (7943), 345–354 (2023) .
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