Medical Iamge Segmentation、Uncertainty Estimation

该站点整理了“医学图像分割、不确定性相关”方向的论文、代码、博客等学习资源

一、论文

Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation (2014), arXiv:1411.4038 [cs.CV]（FCN）
O. Ronneberger, P. Fischer, and T. Brox, “U-Net: Convolutional networks for biomedical image segmentation,” in Proc. MICCAI. Cham, Switzerland: Springer, 2015, pp. 234–241.（UNet）
Zhou Z, Rahman Siddiquee M M, Tajbakhsh N, et al. Unet++: A nested u-net architecture for medical image segmentation[C]//Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support: 4th International Workshop, DLMIA 2018, and 8th International Workshop, ML-CDS 2018, Held in Conjunction with MICCAI 2018, Granada, Spain, September 20, 2018, Proceedings 4. Springer International Publishing, 2018: 3-11.(UNet++)
Qin X, Zhang Z, Huang C, et al. U2-Net: Going deeper with nested U-structure for salient object detection[J]. Pattern recognition, 2020, 106: 107404.(U2-Net)
Chen L C, Papandreou G, Schroff F, et al. Rethinking atrous convolution for semantic image segmentation[J]. arXiv preprint arXiv:1706.05587, 2017.(Deeplab_v3)

Fan D P, Zhou T, Ji G P, et al. Inf-net: Automatic covid-19 lung infection segmentation from ct images[J]. IEEE Transactions on Medical Imaging, 2020, 39(8): 2626-2637.(COVID19感染区域分割)
Fan D P, Ji G P, Zhou T, et al. Pranet: Parallel reverse attention network for polyp segmentation[C]//Medical Image Computing and Computer Assisted Intervention–MICCAI 2020: 23rd International Conference, Lima, Peru, October 4–8, 2020, Proceedings, Part VI 23. Springer International Publishing, 2020: 263-273.（息肉分割）
Oktay O, Schlemper J, Folgoc L L, et al. Attention u-net: Learning where to look for the pancreas[J]. arXiv preprint arXiv:1804.03999, 2018.（注意力UNet：学习在哪里寻找胰腺）
Havaei M, Davy A, Warde-Farley D, et al. Brain tumor segmentation with deep neural networks[J]. Medical image analysis, 2017, 35: 18-31.（脑肿瘤分割）

Gal, Y., & Ghahramani, Z. (2016, June). Dropout as a bayesian approximation: Representing model uncertainty in deep learning. In international conference on machine learning (pp. 1050-1059). PMLR.（将Dropout看做贝叶斯近似的经典论文）
Kendall, A., & Gal, Y. (2017). What uncertainties do we need in bayesian deep learning for computer vision?. Advances in neural information processing systems, 30.（不确定性估计必读论文，将不确定性分为数据不确定性以及模型不确定性，并介绍了在分类和回归中不确定性估计的建模方法）
Louizos, C., & Welling, M. (2017, July). Multiplicative normalizing flows for variational bayesian neural networks. In International Conference on Machine Learning (pp. 2218-2227). PMLR.（变分贝叶斯神经网络，EDL论文中的对比方法）

Lakshminarayanan, B., Pritzel, A., & Blundell, C. (2017). Simple and scalable predictive uncertainty estimation using deep ensembles. Advances in neural information processing systems, 30.（集成方法的开山之作）

Sensoy, M., Kaplan, L., & Kandemir, M. (2018). Evidential deep learning to quantify classification uncertainty. Advances in neural information processing systems, 31.（证据分类）
Sensoy, M., Kaplan, L., Cerutti, F., & Saleki, M. (2020, April). Uncertainty-aware deep classifiers using generative models. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 34, No. 04, pp. 5620-5627).（EDL作者的另一篇论文）
Malinin, A., & Gales, M. (2018). Predictive uncertainty estimation via prior networks. Advances in neural information processing systems, 31.（使用狄利克雷分布建模不确定性的另一种方法）
Ulmer, D. (2021). A survey on evidential deep learning for single-pass uncertainty estimation. arXiv preprint arXiv:2110.03051.（证据不确定性综述）

Zou K, Yuan X, Shen X, et al. EvidenceCap: Towards trustworthy medical image segmentation via evidential identity cap[J]. arXiv preprint arXiv:2301.00349, 2023.
Zou K, Yuan X, Shen X, et al. TBraTS: Trusted brain tumor segmentation[C]//Medical Image Computing and Computer Assisted Intervention–MICCAI 2022: 25th International Conference, Singapore, September 18–22, 2022, Proceedings, Part VIII. Cham: Springer Nature Switzerland, 2022: 503-513.（可信的脑肿瘤分割）