标题/作者	年份
Transunet: Transformers make strong encoders for medical image segmentation J Chen, Y Lu, Q Yu, X Luo, E Adeli, Y Wang, L Lu, AL Yuille, Y Zhou arXiv preprint arXiv:2102.04306	2021
Inf-net: Automatic covid-19 lung infection segmentation from ct images DP Fan, T Zhou, GP Ji, Y Zhou, G Chen, H Fu, J Shen, L Shao IEEE transactions on medical imaging 39 (8), 2626-2637	2020
Swin-unet: Unet-like pure transformer for medical image segmentation H Cao, Y Wang, J Chen, D Jiang, X Zhang, Q Tian, M Wang European conference on computer vision, 205-218	2022
The liver tumor segmentation benchmark (lits) P Bilic, P Christ, HB Li, E Vorontsov, A Ben-Cohen, G Kaissis, A Szeskin, ... Medical Image Analysis 84, 102680	2023
Embracing imperfect datasets: A review of deep learning solutions for medical image segmentation N Tajbakhsh, L Jeyaseelan, Q Li, JN Chiang, Z Wu, X Ding Medical Image Analysis 63, 101693	2020
Unetr: Transformers for 3d medical image segmentation A Hatamizadeh, Y Tang, V Nath, D Yang, A Myronenko, B Landman, ... Proceedings of the IEEE/CVF winter conference on applications of computer …	2022
Medical transformer: Gated axial-attention for medical image segmentation JMJ Valanarasu, P Oza, I Hacihaliloglu, VM Patel Medical Image Computing and Computer Assisted Intervention–MICCAI 2021: 24th …	2021
U-net and its variants for medical image segmentation: A review of theory and applications N Siddique, S Paheding, CP Elkin, V Devabhaktuni Ieee Access 9, 82031-82057	2021
HyperDense-Net: a hyper-densely connected CNN for multi-modal image segmentation J Dolz, K Gopinath, J Yuan, H Lombaert, C Desrosiers, IB Ayed IEEE transactions on medical imaging 38 (5), 1116-1126	2018
Multi-scale self-guided attention for medical image segmentation A Sinha, J Dolz IEEE journal of biomedical and health informatics 25 (1), 121-130	2020
Fully dense UNet for 2-D sparse photoacoustic tomography artifact removal S Guan, AA Khan, S Sikdar, PV Chitnis IEEE journal of biomedical and health informatics 24 (2), 568-576	2019
Med3d: Transfer learning for 3d medical image analysis S Chen, K Ma, Y Zheng arXiv preprint arXiv:1904.00625	2019
The state of the art in kidney and kidney tumor segmentation in contrast-enhanced CT imaging: Results of the KiTS19 challenge N Heller, F Isensee, KH Maier-Hein, X Hou, C Xie, F Li, Y Nan, G Mu, ... Medical image analysis 67, 101821	2021
Transformation-consistent self-ensembling model for semisupervised medical image segmentation X Li, L Yu, H Chen, CW Fu, L Xing, PA Heng IEEE Transactions on Neural Networks and Learning Systems 32 (2), 523-534	2020
3D deep learning on medical images: a review SP Singh, L Wang, S Gupta, H Goli, P Padmanabhan, B Gulyás Sensors 20 (18), 5097	2020
RA-UNet: A hybrid deep attention-aware network to extract liver and tumor in CT scans Q Jin, Z Meng, C Sun, H Cui, R Su Frontiers in Bioengineering and Biotechnology 8, 605132	2020
A review of deep-learning-based medical image segmentation methods X Liu, L Song, S Liu, Y Zhang Sustainability 13 (3), 1224	2021
Modified U-Net (mU-Net) with incorporation of object-dependent high level features for improved liver and liver-tumor segmentation in CT images H Seo, C Huang, M Bassenne, R Xiao, L Xing IEEE transactions on medical imaging 39 (5), 1316-1325	2019
Loss odyssey in medical image segmentation J Ma, J Chen, M Ng, R Huang, Y Li, C Li, X Yang, AL Martel Medical Image Analysis 71, 102035	2021
Semi-supervised semantic segmentation needs strong, varied perturbations G French, S Laine, T Aila, M Mackiewicz, G Finlayson arXiv preprint arXiv:1906.01916	2019

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