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Vision Transformers (ViT)s have shown great performance in self-supervised learning of global and local representations that can be transferred to downstream applications.
Accuracy of ct colonography for detection of large adenomas and cancers
C Daniel Johnson, Mei-Hsiu Chen, Alicia Y Toledano, Jay P Heiken, Abraham Dachman, Mark D Kuo, Christine O Menias, Betina Siewert, Jugesh I Cheema, Richard G Obregon, et al · 2008
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Imagenet: A large-scale hierarchical image database
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei · 2009
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The lung image database consortium (lidc) and image database resource initiative (idri): a completed reference database of lung nodules on ct scans
Samuel G Armato III, Geoffrey McLennan, Luc Bidaut, Michael F McNitt-Gray, Charles R Meyer, Anthony P Reeves, Binsheng Zhao, Denise R Aberle, Claudia I Henschke, Eric A Hoffman, et al · 2011
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Miccai multi-atlas labeling beyond the cranial vault–workshop and challenge
B Landman, Z Xu, J Igelsias, M Styner, T Langerak, and A Klein · 2015
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3d u-net: learning dense volumetric segmentation from sparse annotation
Özgün Çiçek, Ahmed Abdulkadir, Soeren S Lienkamp, Thomas Brox, and Olaf Ronneberger · 2016
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Unsupervised learning of visual representations by solving jigsaw puzzles
Mehdi Noroozi and Paolo Favaro · 2016
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Context encoders: Feature learning by inpainting
Deepak Pathak, Philipp Krahenbuhl, Jeff Donahue, Trevor Darrell, and Alexei A Efros · 2016
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Instance normalization: The missing ingredient for fast stylization
Dmitry Ulyanov, Andrea Vedaldi, and Victor Lempitsky · 2016
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Feature pyramid networks for object detection
Tsung-Yi Lin, Piotr Dollár, Ross Girshick, Kaiming He, Bharath Hariharan, and Serge Belongie · 2017
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Validation, comparison, and combination of algorithms for automatic detection of pulmonary nodules in computed tomography images: the luna16 challenge
Arnaud Arindra Adiyoso Setio, Alberto Traverso, Thomas De Bel, Moira SN Berens, Cas Van Den Bogaard, Piergiorgio Cerello, Hao Chen, Qi Dou, Maria Evelina Fantacci, Bram Geurts, et al · 2017
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Encoder-decoder with atrous separable convolution for semantic image segmentation
Liang-Chieh Chen, Yukun Zhu, George Papandreou, Florian Schroff, and Hartwig Adam · 2018
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Encoder-decoder with atrous separable convolution for semantic image segmentation
Liang-Chieh Chen, Yukun Zhu, George Papandreou, Florian Schroff, and Hartwig Adam · 2018
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Bert: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova · 2018
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Unsupervised representation learning by predicting image rotations
Spyros Gidaris, Praveer Singh, and Nikos Komodakis · 2018
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Imaging and clinical data archive for head and neck squamous cell carcinoma patients treated with radiotherapy
Aaron J Grossberg, Abdallah SR Mohamed, Hesham Elhalawani, William C Bennett, Kirk E Smith, Tracy S Nolan, Bowman Williams, Sasikarn Chamchod, Jolien Heukelom, Michael E Kantor, et al · 2018
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Decoupled weight decay regularization
Ilya Loshchilov and Frank Hutter · 2018
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Stanislav Nikolov, Sam Blackwell, Alexei Zverovitch, Ruheena Mendes, Michelle Livne, Jeffrey De Fauw, Yojan Patel, Clemens Meyer, Harry Askham, Bernardino Romera-Paredes, et al · 2018
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Representation learning with contrastive predictive coding
Aaron van den Oord, Yazhe Li, and Oriol Vinyals · 2018
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A multi-scale pyramid of 3d fully convolutional networks for abdominal multi-organ segmentation
Holger R Roth, Chen Shen, Hirohisa Oda, Takaaki Sugino, Masahiro Oda, Yuichiro Hayashi, Kazunari Misawa, and Kensaku Mori · 2018
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Sniper: Efficient multi-scale training
Bharat Singh, Mahyar Najibi, and Larry S Davis · 2018
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Self-supervised learning for medical image analysis using image context restoration
Liang Chen, Paul Bentley, Kensaku Mori, Kazunari Misawa, Michitaka Fujiwara, and Daniel Rueckert · 2019
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Med3d: Transfer learning for 3d medical image analysis
Sihong Chen, Kai Ma, and Yefeng Zheng · 2019
Cited alongside, same era.
Scalable neural architecture search for 3d medical image segmentation
Sungwoong Kim, Ildoo Kim, Sungbin Lim, Woonhyuk Baek, Chiheon Kim, Hyungjoo Cho, Boogeon Yoon, and Taesup Kim · 2019
Cited alongside, same era.
Transfusion: Understanding transfer learning for medical imaging
Maithra Raghu, Chiyuan Zhang, Jon Kleinberg, and Samy Bengio · 2019
Cited alongside, same era.
Amber L Simpson, Michela Antonelli, Spyridon Bakas, Michel Bilello, Keyvan Farahani, Bram Van Ginneken, Annette Kopp-Schneider, Bennett A Landman, Geert Litjens, Bjoern Menze, et al · 2019
Cited alongside, same era.
Prior-aware neural network for partially-supervised multi-organ segmentation
Yuyin Zhou, Zhe Li, Song Bai, Chong Wang, Xinlei Chen, Mei Han, Elliot Fishman, and Alan L Yuille · 2019
Per-pixel classification is not all you need for semantic segmentation
Bowen Cheng, Alexander G Schwing, and Alexander Kirillov · 2021
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Up-detr: Unsupervised pre-training for object detection with transformers
Zhigang Dai, Bolun Cai, Yugeng Lin, and Junying Chen · 2021
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Transferable visual words: Exploiting the semantics of anatomical patterns for self-supervised learning
Fatemeh Haghighi, Mohammad Reza Hosseinzadeh Taher, Zongwei Zhou, Michael B Gotway, and Jianming Liang · 2021
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Dints: Differentiable neural network topology search for 3d medical image segmentation
Yufan He, Dong Yang, Holger Roth, Can Zhao, and Daguang Xu · 2021
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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
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Cited alongside, same era.
A simple framework for contrastive learning of visual representations
Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey Hinton · 2020
Cited alongside, same era.
Chest imaging representing a covid-19 positive rural us population
Shivang Desai, Ahmad Baghal, Thidathip Wongsurawat, Piroon Jenjaroenpun, Thomas Powell, Shaymaa Al-Shukri, Kim Gates, Phillip Farmer, Michael Rutherford, Geri Blake, et al · 2020
Cited alongside, same era.
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
Cited alongside, same era.
Momentum contrast for unsupervised visual representation learning
Kaiming He, Haoqi Fan, Yuxin Wu, Saining Xie, and Ross Girshick · 2020
Cited alongside, same era.
Contrastive learning for unpaired image-to-image translation
Taesung Park, Alexei A Efros, Richard Zhang, and Jun-Yan Zhu · 2020
Cited alongside, same era.
3d self-supervised methods for medical imaging
Aiham Taleb, Winfried Loetzsch, Noel Danz, Julius Severin, Thomas Gaertner, Benjamin Bergner, and Christoph Lippert · 2020
Cited alongside, same era.
Self-supervised learning of pixel-wise anatomical embeddings in radiological images
Ke Yan, Jinzheng Cai, Dakai Jin, Shun Miao, Adam P Harrison, Dazhou Guo, Youbao Tang, Jing Xiao, Jingjing Lu, and Le Lu · 2020
Cited alongside, same era.
Medical transformer: gated axial-attention for medical image segmentation
JM Jose and P Oza · 2021
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Swinir: Image restoration using swin transformer
Jingyun Liang, Jiezhang Cao, Guolei Sun, Kai Zhang, Luc Van Gool, and Radu Timofte · 2021
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Ds-transunet: Dual swin transformer u-net for medical image segmentation
Ailiang Lin, Bingzhi Chen, Jiayu Xu, Zheng Zhang, and Guangming Lu · 2021
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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
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Ze Liu, Jia Ning, Yue Cao, Yixuan Wei, Zheng Zhang, Stephen Lin, and Han Hu · 2021
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Do vision transformers see like convolutional neural networks?
Maithra Raghu, Thomas Unterthiner, Simon Kornblith, Chiyuan Zhang, and Alexey Dosovitskiy · 2021
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Body part regression with self-supervision
Yucheng Tang, Riqiang Gao, Shizhong Han, Yunqiang Chen, Dashan Gao, Vishwesh Nath, Camilo Bermudez, Michael R Savona, Shunxing Bao, Ilwoo Lyu, et al · 2021
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High-resolution 3d abdominal segmentation with random patch network fusion
Yucheng Tang, Riqiang Gao, Ho Hin Lee, Shizhong Han, Yunqiang Chen, Dashan Gao, Vishwesh Nath, Camilo Bermudez, Michael R Savona, Richard G Abramson, et al · 2021
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Self-supervised image-text pre-training with mixed data in chest x-rays
Xiaosong Wang, Ziyue Xu, Leo Tam, Dong Yang, and Daguang Xu · 2021
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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
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Self-supervised learning with swin transformers
Zhenda Xie, Yutong Lin, Zhuliang Yao, Zheng Zhang, Qi Dai, Yue Cao, and Han Hu · 2021
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Levit-unet: Make faster encoders with transformer for medical image segmentation
Guoping Xu, Xingrong Wu, Xuan Zhang, and Xinwei He · 2021
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Xiaohua Zhai, Alexander Kolesnikov, Neil Houlsby, and Lucas Beyer · 2021
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Rethinking semantic segmentation from a sequence-to-sequence perspective with transformers
Sixiao Zheng, Jiachen Lu, Hengshuang Zhao, Xiatian Zhu, Zekun Luo, Yabiao Wang, Yanwei Fu, Jianfeng Feng, Tao Xiang, Philip HS Torr, et al · 2021
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nnformer: Interleaved transformer for volumetric segmentation
Hong-Yu Zhou, Jiansen Guo, Yinghao Zhang, Lequan Yu, Liansheng Wang, and Yizhou Yu · 2021
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Models genesis
Zongwei Zhou, Vatsal Sodha, Jiaxuan Pang, Michael B Gotway, and Jianming Liang · 2021
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Unetr: Transformers for 3d medical image segmentation
Ali Hatamizadeh, Yucheng Tang, Vishwesh Nath, Dong Yang, Andriy Myronenko, Bennett Landman, Holger R Roth, and Daguang Xu · 2022
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