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Bayesian Neural Networks for 2D MRI Segmentation (2311.14875v3)
Published 24 Nov 2023 in eess.IV and cs.CV
Abstract: Uncertainty quantification is vital for safety-critical Deep Learning applications like medical image segmentation. We introduce BA U-Net, an uncertainty-aware model for MRI segmentation that integrates Bayesian Neural Networks with Attention Mechanisms. BA U-Net delivers accurate, interpretable results, crucial for reliable pathology screening. Evaluated on BraTS 2020, this model addresses the critical need for confidence estimation in deep learning-based medical imaging.
- Olaf Ronneberger, Philipp Fischer and Thomas Brox “U-Net: Convolutional Networks for Biomedical Image Segmentation” In Medical Image Computing and Computer-Assisted Intervention – MICCAI 2015 Cham: Springer International Publishing, 2015, pp. 234–241
- “What uncertainties do we need in bayesian deep learning for computer vision?” In Advances in neural information processing systems 30, 2017
- Nabeel Seedat “MCU-Net: A framework towards uncertainty representations for decision support system patient referrals in healthcare contexts” In CoRR abs/2007.03995, 2020 arXiv: https://arxiv.org/abs/2007.03995
- Christopher M. Bishop “Pattern Recognition and Machine Learning (Information Science and Statistics)” Berlin, Heidelberg: Springer-Verlag, 2006
- Balaji Lakshminarayanan, Alexander Pritzel and Charles Blundell “Simple and scalable predictive uncertainty estimation using deep ensembles” In Advances in neural information processing systems 30, 2017
- “Dropout as a bayesian approximation: Representing model uncertainty in deep learning” In international conference on machine learning, 2016, pp. 1050–1059 PMLR
- “Dropout: a simple way to prevent neural networks from overfitting” In The journal of machine learning research 15.1 JMLR. org, 2014, pp. 1929–1958
- Ian Osband “Risk versus uncertainty in deep learning: Bayes, bootstrap and the dangers of dropout” In NIPS workshop on bayesian deep learning 192, 2016
- “Is MC Dropout Bayesian?” In arXiv preprint arXiv:2110.04286, 2021
- “Bayesian layers: A module for neural network uncertainty” In Advances in neural information processing systems 32, 2019
- Abhinav Sagar “Uncertainty Quantification using Variational Inference for Biomedical Image Segmentation”, 2021 arXiv:2008.07588 [eess.IV]
- “The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS)” In IEEE Transactions on Medical Imaging 34.10, 2015, pp. 1993–2024 DOI: 10.1109/TMI.2014.2377694
- “Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge”, 2019 arXiv:1811.02629 [cs.CV]
- Jonathan Long, Evan Shelhamer and Trevor Darrell “Fully Convolutional Networks for Semantic Segmentation” In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015
- “Attention u-net: Learning where to look for the pancreas” In arXiv preprint arXiv:1804.03999, 2018
- “CBAM: Convolutional Block Attention Module” In Proceedings of the European Conference on Computer Vision (ECCV), 2018
- “Sa-unet: Spatial attention u-net for retinal vessel segmentation” In 2020 25th international conference on pattern recognition (ICPR), 2021, pp. 1236–1242 IEEE
- “Bayesian convolutional neural networks with Bernoulli approximate variational inference” In arXiv preprint arXiv:1506.02158, 2015
- “Uncertainty-aware deep learning in healthcare: a scoping review” In PLOS digital health 1.8 Public Library of Science San Francisco, CA USA, 2022, pp. e0000085
- Golnaz Ghiasi, Tsung-Yi Lin and Quoc V Le “Dropblock: A regularization method for convolutional networks” In Advances in neural information processing systems 31, 2018
- “Monte Carlo DropBlock for modelling uncertainty in object detection” In Pattern Recognition Elsevier, 2023, pp. 110003
- “Notes on the behavior of mc dropout” In arXiv preprint arXiv:2008.02627, 2020
- “Weight uncertainty in neural network” In International conference on machine learning, 2015, pp. 1613–1622 PMLR
- Alex Graves “Practical variational inference for neural networks” In Advances in neural information processing systems 24, 2011
- Kingma and Welling “Auto-encoding variational bayes” In arXiv preprint arXiv:1312.6114, 2013
- Durk P Kingma, Tim Salimans and Max Welling “Variational dropout and the local reparameterization trick” In Advances in neural information processing systems 28, 2015
- “Flipout: Efficient Pseudo-Independent Weight Perturbations on Mini-Batches” In CoRR abs/1803.04386, 2018 arXiv: http://arxiv.org/abs/1803.04386
- “Bi-directional ConvLSTM U-Net with densley connected convolutions” In Proceedings of the IEEE/CVF international conference on computer vision workshops, 2019, pp. 0–0
- “Tensorflow distributions” In arXiv preprint arXiv:1711.10604, 2017
- “The SRI24 multichannel atlas of normal adult human brain structure” In Human brain mapping 31.5 Wiley Online Library, 2010, pp. 798–819
- Kingma and Ba “Adam: A method for stochastic optimization” In arXiv preprint arXiv:1412.6980, 2014
- “The Rician distribution of noisy MRI data” In Magn Reson Med 34.6, 1995, pp. 910–914