Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
125 tokens/sec
GPT-4o
53 tokens/sec
Gemini 2.5 Pro Pro
42 tokens/sec
o3 Pro
4 tokens/sec
GPT-4.1 Pro
47 tokens/sec
DeepSeek R1 via Azure Pro
28 tokens/sec
2000 character limit reached

UniCell: Universal Cell Nucleus Classification via Prompt Learning (2402.12938v1)

Published 20 Feb 2024 in cs.CV

Abstract: The recognition of multi-class cell nuclei can significantly facilitate the process of histopathological diagnosis. Numerous pathological datasets are currently available, but their annotations are inconsistent. Most existing methods require individual training on each dataset to deduce the relevant labels and lack the use of common knowledge across datasets, consequently restricting the quality of recognition. In this paper, we propose a universal cell nucleus classification framework (UniCell), which employs a novel prompt learning mechanism to uniformly predict the corresponding categories of pathological images from different dataset domains. In particular, our framework adopts an end-to-end architecture for nuclei detection and classification, and utilizes flexible prediction heads for adapting various datasets. Moreover, we develop a Dynamic Prompt Module (DPM) that exploits the properties of multiple datasets to enhance features. The DPM first integrates the embeddings of datasets and semantic categories, and then employs the integrated prompts to refine image representations, efficiently harvesting the shared knowledge among the related cell types and data sources. Experimental results demonstrate that the proposed method effectively achieves the state-of-the-art results on four nucleus detection and classification benchmarks. Code and models are available at https://github.com/lhaof/UniCell

Definition Search Book Streamline Icon: https://streamlinehq.com
References (54)
  1. Multi-class cell detection using spatial context representation. In ICCV, 4005–4014.
  2. Slicing aided hyper inference and fine-tuning for small object detection. In ICIP, 966–970. IEEE.
  3. NuCLS: A scalable crowdsourcing approach and dataset for nucleus classification and segmentation in breast cancer. GigaScience, 11: giac037.
  4. Layer normalization. arXiv preprint arXiv:1607.06450.
  5. End-to-end object detection with transformers. In ECCV, 213–229.
  6. CPP-net: Context-aware polygon proposal network for nucleus segmentation. IEEE Transactions on Image Processing, 32: 980–994.
  7. Omnipose: a high-precision morphology-independent solution for bacterial cell segmentation. Nature Methods, 19(11): 1438–1448.
  8. Imagenet: A large-scale hierarchical image database. In CVPR, 248–255. IEEE.
  9. SONNET: A self-guided ordinal regression neural network for segmentation and classification of nuclei in large-scale multi-tissue histology images. IEEE Journal of Biomedical and Health Informatics, 26(7): 3218–3228.
  10. Colorectal carcinoma: Pathologic aspects. Journal of Gastrointestinal Oncology, 3(3): 153.
  11. The immune contexture in human tumours: impact on clinical outcome. Nature Reviews Cancer, 12(4): 298–306.
  12. Multiple instance captioning: Learning representations from histopathology textbooks and articles. In CVPR, 16549–16559.
  13. Lizard: a large-scale dataset for colonic nuclear instance segmentation and classification. In ICCV, 684–693.
  14. One model is all you need: multi-task learning enables simultaneous histology image segmentation and classification. Medical Image Analysis, 83: 102685.
  15. Hover-net: Simultaneous segmentation and classification of nuclei in multi-tissue histology images. Medical Image Analysis, 58: 101563.
  16. Mask RCNN algorithm for nuclei detection on breast cancer histopathological images. International Journal of Imaging Systems and Technology, 32(1): 209–217.
  17. Prompt-based grouping transformer for nucleus detection and classification. In MICCAI, 569–579. Springer.
  18. Affine-Consistent Transformer for Multi-Class Cell Nuclei Detection. In Proceedings of the IEEE/CVF International Conference on Computer Vision, 21384–21393.
  19. Oneformer: One transformer to rule universal image segmentation. In CVPR, 2989–2998.
  20. Visual prompt tuning. In ECCV, 709–727. Springer.
  21. Maple: Multi-modal prompt learning. In CVPR, 19113–19122.
  22. Kuhn, H. W. 1955. The Hungarian method for the assignment problem. Naval Research Logistics Quarterly, 2(1-2): 83–97.
  23. Focal loss for dense object detection. In ICCV, 2980–2988.
  24. Swin transformer: Hierarchical vision transformer using shifted windows. In ICCV, 10012–10022.
  25. A convnet for the 2020s. In CVPR, 11976–11986.
  26. Which pixel to annotate: a label-efficient nuclei segmentation framework. IEEE Transactions on Medical Imaging, 42(4): 947–958.
  27. Structure Embedded Nucleus Classification for Histopathology Images. arXiv preprint arXiv:2302.11416.
  28. Multi-stream Cell Segmentation with Low-level Cues for Multi-modality Images. In Competitions in Neural Information Processing Systems, 1–10. PMLR.
  29. The Multi-modality Cell Segmentation Challenge: Towards Universal Solutions. arXiv preprint arXiv:2308.05864.
  30. Applying watershed algorithms to the segmentation of clustered nuclei. Cytometry: The Journal of the International Society for Analytical Cytology, 28(4): 289–297.
  31. Multi-task pre-training of deep neural networks for digital pathology. IEEE Journal of Biomedical and Health Informatics, 25(2): 412–421.
  32. Automated detection of cell nuclei in pap smear images using morphological reconstruction and clustering. IEEE Transactions on Information Technology in Biomedicine, 15(2): 233–241.
  33. A multi-task feature fusion model for cervical cell classification. IEEE Journal of Biomedical and Health Informatics, 26(9): 4668–4678.
  34. U-net: Convolutional networks for biomedical image segmentation. In MICCAI, 234–241. Springer.
  35. OCELOT: Overlapped Cell on Tissue Dataset for Histopathology. In CVPR, 23902–23912.
  36. Computer-aided diagnosis of breast cancer using cytological images: A systematic review. Tissue and Cell, 48(5): 461–474.
  37. Neural machine translation of rare words with subword units. ACL.
  38. Locality sensitive deep learning for detection and classification of nuclei in routine colon cancer histology images. IEEE Transactions on Medical Imaging, 35(5): 1196–1206.
  39. Cellpose: a generalist algorithm for cellular segmentation. Nature Methods, 18(1): 100–106.
  40. Multi-class cell detection using modified self-attention. In CVPRW, 1855–1863.
  41. Extending unsupervised neural image compression with supervised multitask learning. In Medical Imaging with Deep Learning, 770–783. PMLR.
  42. MoNuSAC2020: A multi-organ nuclei segmentation and classification challenge. IEEE Transactions on Medical Imaging, 40(12): 3413–3423.
  43. Transpath: Transformer-based self-supervised learning for histopathological image classification. In MICCAI, 186–195. Springer.
  44. Global and local attentional feature alignment for domain adaptive nuclei detection in histopathology images. Artificial Intelligence in Medicine, 132: 102341.
  45. Recent advances of deep learning for computational histopathology: principles and applications. Cancers, 14(5): 1199.
  46. Automatic nuclei detection based on generalized laplacian of gaussian filters. IEEE Journal of Biomedical and Health Informatics, 21(3): 826–837.
  47. CS-CO: A Hybrid Self-Supervised Visual Representation Learning Method for H&E-stained Histopathological Images. Medical Image Analysis, 81: 102539.
  48. Diffusion-based data augmentation for nuclei image segmentation. In MICCAI, 592–602. Springer.
  49. Breast cancer intelligent analysis of histopathological data: A systematic review. Applied Soft Computing, 113: 107886.
  50. Dino: Detr with improved denoising anchor boxes for end-to-end object detection. ICLR.
  51. Merging nucleus datasets by correlation-based cross-training. Medical Image Analysis, 84: 102705.
  52. Conditional prompt learning for vision-language models. In CVPR, 16816–16825.
  53. Learning to prompt for vision-language models. International Journal of Computer Vision, 130(9): 2337–2348.
  54. Deformable detr: Deformable transformers for end-to-end object detection. ICLR.

Summary

We haven't generated a summary for this paper yet.

Ai Sparkles Streamline Icon: https://streamlinehq.com Summarize Now