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The application of 3D ViTs to medical image segmentation has seen remarkable strides, somewhat overshadowing the budding advancements in Convolutional Neural Network (CNN)-based models.
U-net: Convolutional networks for biomedical image segmentation
Olaf Ronneberger, Philipp Fischer, and Thomas Brox · 2015
Earlier work this paper cites.
3d u-net: learning dense volumetric segmentation from sparse annotation
Özgün Çiçek, Ahmed Abdulkadir, Soeren S Lienkamp, Thomas Brox, and Olaf Ronneberger · 2016
Earlier work this paper cites.
V-net: Fully convolutional neural networks for volumetric medical image segmentation
Fausto Milletari, Nassir Navab, and Seyed-Ahmad Ahmadi · 2016
Earlier work this paper cites.
H-denseunet: hybrid densely connected unet for liver and tumor segmentation from ct volumes
Xiaomeng Li, Hao Chen, Xiaojuan Qi, Qi Dou, Chi-Wing Fu, and Pheng-Ann Heng · 2018
Earlier work this paper cites.
3d mri brain tumor segmentation using autoencoder regularization
Andriy Myronenko · 2018
Earlier work this paper cites.
Unet++: A nested u-net architecture for medical image segmentation
Zongwei Zhou, Md Mahfuzur Rahman Siddiquee, Nima Tajbakhsh, and Jianming Liang · 2018
Earlier work this paper cites.
Nicholas Heller, Niranjan Sathianathen, Arveen Kalapara, Edward Walczak, Keenan Moore, Heather Kaluzniak, Joel Rosenberg, Paul Blake, Zachary Rengel, Makinna Oestreich, et al · 2019
Earlier work this paper cites.
An image is worth 16x16 words: Transformers for image recognition at scale
Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, et al · 2020
Earlier work this paper cites.
Deformable 3d convolution for video super-resolution
Xinyi Ying, Longguang Wang, Yingqian Wang, Weidong Sheng, Wei An, and Yulan Guo · 2020
Earlier work this paper cites.
Swin-unet: Unet-like pure transformer for medical image segmentation
Hu Cao, Yueyue Wang, Joy Chen, Dongsheng Jiang, Xiaopeng Zhang, Qi Tian, and Manning Wang · 2021
Earlier work this paper cites.
Transunet: Transformers make strong encoders for medical image segmentation
Jieneng Chen, Yongyi Lu, Qihang Yu, Xiangde Luo, Ehsan Adeli, Yan Wang, Le Lu, Alan L Yuille, and Yuyin Zhou · 2021
Cited alongside, same era.
nnu-net: a self-configuring method for deep learning-based biomedical image segmentation
Fabian Isensee, Paul F Jaeger, Simon AA Kohl, Jens Petersen, and Klaus H Maier-Hein · 2021
Cited alongside, same era.
Rap-net: Coarse-to-fine multi-organ segmentation with single random anatomical prior
Ho Hin Lee, Yucheng Tang, Shunxing Bao, Richard G Abramson, Yuankai Huo, and Bennett A Landman · 2021
Cited alongside, same era.
Swin transformer: Hierarchical vision transformer using shifted windows
Ze Liu, Yutong Lin, Yue Cao, Han Hu, Yixuan Wei, Zheng Zhang, Stephen Lin, and Baining Guo · 2021
Cited alongside, same era.
Transbts: Multimodal brain tumor segmentation using transformer
Wenxuan Wang, Chen Chen, Meng Ding, Hong Yu, Sen Zha, and Jiangyun Li · 2021
Cited alongside, same era.
Amos: A large-scale abdominal multi-organ benchmark for versatile medical image segmentation
Yuanfeng Ji, Haotian Bai, Jie Yang, Chongjian Ge, Ye Zhu, Ruimao Zhang, Zhen Li, Lingyan Zhang, Wanling Ma, Xiang Wan, et al · 2022
Later among the works it cites.
Swinbts: a method for 3d multimodal brain tumor segmentation using swin transformer
Yun Jiang, Yuan Zhang, Xin Lin, Jinkun Dong, Tongtong Cheng, and Jing Liang · 2022
Later among the works it cites.
3d ux-net: A large kernel volumetric convnet modernizing hierarchical transformer for medical image segmentation
Ho Hin Lee, Shunxing Bao, Yuankai Huo, and Bennett A Landman · 2022
Later among the works it cites.
A convnet for the 2020s
Zhuang Liu, Hanzi Mao, Chao-Yuan Wu, Christoph Feichtenhofer, Trevor Darrell, and Saining Xie · 2022
Later among the works it cites.
Self-supervised pre-training of swin transformers for 3d medical image analysis
Yucheng Tang, Dong Yang, Wenqi Li, Holger R Roth, Bennett Landman, Daguang Xu, Vishwesh Nath, and Ali Hatamizadeh · 2022
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Cotr: Efficiently bridging cnn and transformer for 3d medical image segmentation
Yutong Xie, Jianpeng Zhang, Chunhua Shen, and Yong Xia · 2021
Cited alongside, same era.
Levit-unet: Make faster encoders with transformer for medical image segmentation
Guoping Xu, Xingrong Wu, Xuan Zhang, and Xinwei He · 2021
Cited alongside, same era.
nnformer: Interleaved transformer for volumetric segmentation
Hong-Yu Zhou, Jiansen Guo, Yinghao Zhang, Lequan Yu, Liansheng Wang, and Yizhou Yu · 2021
Cited alongside, same era.
The medical segmentation decathlon
Michela Antonelli, Annika Reinke, Spyridon Bakas, Keyvan Farahani, Annette Kopp-Schneider, Bennett A Landman, Geert Litjens, Bjoern Menze, Olaf Ronneberger, Ronald M Summers, et al · 2022
Cited alongside, same era.
Re-parameterizing your optimizers rather than architectures
Xiaohan Ding, Honghao Chen, Xiangyu Zhang, Kaiqi Huang, Jungong Han, and Guiguang Ding
Cited in the paper.
Scaling up your kernels to 31x31: Revisiting large kernel design in cnns
Xiaohan Ding, Xiangyu Zhang, Jungong Han, and Guiguang Ding
Cited in the paper.
Swin unetr: Swin transformers for semantic segmentation of brain tumors in mri images
Ali Hatamizadeh, Vishwesh Nath, Yucheng Tang, Dong Yang, Holger R Roth, and Daguang Xu
Cited in the paper.
Later among the works it cites.
Towards simultaneous segmentation of liver tumors and intrahepatic vessels via cross-attention mechanism
Haopeng Kuang, Dingkang Yang, Shunli Wang, Xiaoying Wang, and Lihua Zhang · 2023
Closest in time.
Scaling up 3d kernels with bayesian frequency re-parameterization for medical image segmentation
Ho Hin Lee, Quan Liu, Shunxing Bao, Qi Yang, Xin Yu, Leon Y Cai, Thomas Li, Yuankai Huo, Xenofon Koutsoukos, and Bennett A Landman · 2023
Closest in time.
Internimage: Exploring large-scale vision foundation models with deformable convolutions
Wenhai Wang, Jifeng Dai, Zhe Chen, Zhenhang Huang, Zhiqi Li, Xizhou Zhu, Xiaowei Hu, Tong Lu, Lewei Lu, Hongsheng Li, et al · 2023
Closest in time.
Unest: local spatial representation learning with hierarchical transformer for efficient medical segmentation
Xin Yu, Qi Yang, Yinchi Zhou, Leon Y Cai, Riqiang Gao, Ho Hin Lee, Thomas Li, Shunxing Bao, Zhoubing Xu, Thomas A Lasko, et al · 2023
Closest in time.