Fetching the paper…
Reading the bibliography…
Owing to success in the data-rich domain of natural images, Transformers have recently become popular in medical image segmentation.
Decoupled weight decay regularization
Ilya Loshchilov and Frank Hutter · 2017
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.
Transformers in vision: A survey
Salman Khan, Muzammal Naseer, Munawar Hayat, Syed Waqas Zamir, Fahad Shahbaz Khan, and Mubarak Shah · 2021
Earlier work this paper cites.
Tianyang Lin, Yuxin Wang, Xiangyang Liu, and Xipeng Qiu · 2021
Earlier work this paper cites.
An attentive survey of attention models
Sneha Chaudhari, Varun Mithal, Gungor Polatkan, and Rohan Ramanath · 2021
Earlier work this paper cites.
Utnet: a hybrid transformer architecture for medical image segmentation
Yunhe Gao, Mu Zhou, and Dimitris N Metaxas · 2021
Earlier work this paper cites.
Levit-unet: Make faster encoders with transformer for medical image segmentation
Guoping Xu, Xingrong Wu, Xuan Zhang, and Xinwei He · 2021
Earlier work this paper cites.
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.
U-net transformer: Self and cross attention for medical image segmentation
Olivier Petit, Nicolas Thome, Clement Rambour, Loic Themyr, Toby Collins, and Luc Soler · 2021
Cited alongside, same era.
Unetr: Transformers for 3d medical image segmentation
Ali Hatamizadeh, Yucheng Tang, Vishwesh Nath, Dong Yang, Andriy Myronenko, Bennett Landman, Holger Roth, and Daguang Xu · 2021
Cited alongside, same era.
Self-supervised pre-training of swin transformers for 3d medical image analysis
Yucheng Tang, Dong Yang, Wenqi Li, Holger Roth, Bennett Landman, Daguang Xu, Vishwesh Nath, and Ali Hatamizadeh · 2021
Cited alongside, same era.
Swin transformer: Hierarchical vision transformer using shifted windows
Transbts: Multimodal brain tumor segmentation using transformer
Wenxuan Wang, Chen Chen, Meng Ding, Hong Yu, Sen Zha, and Jiangyun Li · 2021
Later among the works it cites.
Cotr: Efficiently bridging cnn and transformer for 3d medical image segmentation
Yutong Xie, Jianpeng Zhang, Chunhua Shen, and Yong Xia · 2021
Later among the works it 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
Later among the works it cites.
Transfuse: Fusing transformers and cnns for medical image segmentation
Yundong Zhang, Huiye Liu, and Qiang Hu · 2021
Later among the works it cites.
A survey on vision transformer
Kai Han, Yunhe Wang, Hanting Chen, Xinghao Chen, Jianyuan Guo, Zhenhua Liu, Yehui Tang, An Xiao, Chunjing Xu, Yixing Xu, et al · 2022
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Ze Liu, Yutong Lin, Yue Cao, Han Hu, Yixuan Wei, Zheng Zhang, Stephen Lin, and Baining Guo · 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.
Ali Hatamizadeh, Vishwesh Nath, Yucheng Tang, Dong Yang, Holger Roth, and Daguang Xu · 2022
Later among the works it cites.