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Fully Convolutional Neural Networks (FCNNs) with contracting and expanding paths have shown prominence for the majority of medical image segmentation applications since the past decade.
Multi-scale 3d convolutional neural networks for lesion segmentation in brain mri
Konstantinos Kamnitsas, Liang Chen, Christian Ledig, Daniel Rueckert, and Ben Glocker · 2015
Earlier work this paper cites.
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
Earlier work this paper cites.
U-net: Convolutional networks for biomedical image segmentation
O. Ronneberger, P.Fischer, and T. Brox · 2015
Earlier work this paper cites.
Layer normalization
Jimmy Lei Ba, Jamie Ryan Kiros, and Geoffrey E Hinton · 2016
Earlier work this paper cites.
Combining fully convolutional and recurrent neural networks for 3d biomedical image segmentation
Jianxu Chen, Lin Yang, Yizhe Zhang, Mark Alber, and Danny Z Chen · 2016
Earlier work this paper cites.
3d deeply supervised network for automatic liver segmentation from ct volumes
Qi Dou, Hao Chen, Yueming Jin, Lequan Yu, Jing Qin, and Pheng-Ann Heng · 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
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Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs
Liang-Chieh Chen, George Papandreou, Iasonas Kokkinos, Kevin Murphy, and Alan L Yuille · 2017
Earlier work this paper cites.
Efficient multi-scale 3d cnn with fully connected crf for accurate brain lesion segmentation
Konstantinos Kamnitsas, Christian Ledig, Virginia FJ Newcombe, Joanna P Simpson, Andrew D Kane, David K Menon, Daniel Rueckert, and Ben Glocker · 2017
Earlier work this paper cites.
On the compactness, efficiency, and representation of 3d convolutional networks: brain parcellation as a pretext task
Wenqi Li, Guotai Wang, Lucas Fidon, Sebastien Ourselin, M Jorge Cardoso, and Tom Vercauteren · 2017
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Decoupled weight decay regularization
Ilya Loshchilov and Frank Hutter · 2017
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Hierarchical 3d fully convolutional networks for multi-organ segmentation
Holger R Roth, Hirohisa Oda, Yuichiro Hayashi, Masahiro Oda, Natsuki Shimizu, Michitaka Fujiwara, Kazunari Misawa, and Kensaku Mori · 2017
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin · 2017
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Volumetric convnets with mixed residual connections for automated prostate segmentation from 3d mr images
Lequan Yu, Xin Yang, Hao Chen, Jing Qin, and Pheng Ann Heng · 2017
Earlier work this paper cites.
Deeply-supervised cnn for prostate segmentation
Qikui Zhu, Bo Du, Baris Turkbey, Peter L Choyke, and Pingkun Yan · 2017
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Spyridon Bakas, Mauricio Reyes, et Int, and Bjoern Menze · 2018
Earlier work this paper cites.
Encoder-decoder with atrous separable convolution for semantic image segmentation
Liang-Chieh Chen, Yukun Zhu, George Papandreou, Florian Schroff, and Hartwig Adam · 2018
Earlier work this paper cites.
Encoder-decoder with atrous separable convolution for semantic image segmentation
Liang-Chieh Chen, Yukun Zhu, George Papandreou, Florian Schroff, and Hartwig Adam · 2018
Earlier work this paper cites.
Bert: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova · 2018
Earlier work this paper cites.
Automatic multi-organ segmentation on abdominal ct with dense v-networks
Eli Gibson, Francesco Giganti, Yipeng Hu, Ester Bonmati, Steve Bandula, Kurinchi Gurusamy, Brian Davidson, Stephen P Pereira, Matthew J Clarkson, and Dean C Barratt · 2018
Cited alongside, same era.
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
Cited alongside, same era.
3d anisotropic hybrid network: Transferring convolutional features from 2d images to 3d anisotropic volumes
Siqi Liu, Daguang Xu, S Kevin Zhou, Olivier Pauly, Sasa Grbic, Thomas Mertelmeier, Julia Wicklein, Anna Jerebko, Weidong Cai, and Dorin Comaniciu · 2018
Cited alongside, same era.
Attention u-net: Learning where to look for the pancreas
Ozan Oktay, Jo Schlemper, Loic Le Folgoc, Matthew Lee, Mattias Heinrich, Kazunari Misawa, Kensaku Mori, Steven McDonagh, Nils Y Hammerla, Bernhard Kainz, et al · 2018
Cited alongside, same era.
Non-local neural networks
Xiaolong Wang, Ross Girshick, Abhinav Gupta, and Kaiming He · 2018
Multiclass semantic segmentation and quantification of traumatic brain injury lesions on head ct using deep learning: an algorithm development and multicentre validation study
Miguel Monteiro, Virginia FJ Newcombe, Francois Mathieu, Krishma Adatia, Konstantinos Kamnitsas, Enzo Ferrante, Tilak Das, Daniel Whitehouse, Daniel Rueckert, David K Menon, et al · 2020
Later among the works it cites.
3d semi-supervised learning with uncertainty-aware multi-view co-training
Yingda Xia, Fengze Liu, Dong Yang, Jinzheng Cai, Lequan Yu, Zhuotun Zhu, Daguang Xu, Alan Yuille, and Holger Roth · 2020
Later among the works it cites.
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 · 2020
Later among the works it cites.
Deformable detr: Deformable transformers for end-to-end object detection
Xizhou Zhu, Weijie Su, Lewei Lu, Bin Li, Xiaogang Wang, and Jifeng Dai · 2020
Later among the works it cites.
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Cited alongside, same era.
Dual attention network for scene segmentation
Jun Fu, Jing Liu, Haijie Tian, Yong Li, Yongjun Bao, Zhiwei Fang, and Hanqing Lu · 2019
Cited alongside, same era.
Ce-net: Context encoder network for 2d medical image segmentation
Zaiwang Gu, Jun Cheng, Huazhu Fu, Kang Zhou, Huaying Hao, Yitian Zhao, Tianyang Zhang, Shenghua Gao, and Jiang Liu · 2019
Cited alongside, same era.
Nicholas Heller, Niranjan Sathianathen, Arveen Kalapara, Edward Walczak, Keenan Moore, Heather Kaluzniak, Joel Rosenberg, Paul Blake, Zachary Rengel, Makinna Oestreich, et al · 2019
Cited alongside, same era.
Local relation networks for image recognition
Han Hu, Zheng Zhang, Zhenda Xie, and Stephen Lin · 2019
Cited alongside, same era.
An attempt at beating the 3d u-net
Fabian Isensee and Klaus H Maier-Hein · 2019
Cited alongside, same era.
Stand-alone self-attention in vision models
Prajit Ramachandran, Niki Parmar, Ashish Vaswani, Irwan Bello, Anselm Levskaya, and Jonathon Shlens · 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.
Hangbo Bao, Li Dong, and Furu Wei · 2021
Closest in time.
Emerging properties in self-supervised vision transformers
Mathilde Caron, Hugo Touvron, Ishan Misra, Hervé Jégou, Julien Mairal, Piotr Bojanowski, and Armand Joulin · 2021
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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
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Twins: Revisiting the design of spatial attention in vision transformers
Xiangxiang Chu, Zhi Tian, Yuqing Wang, Bo Zhang, Haibing Ren, Xiaolin Wei, Huaxia Xia, and Chunhua Shen · 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
Closest in time.
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
Closest in time.
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
Closest in time.
Training data-efficient image transformers & distillation through attention
Hugo Touvron, Matthieu Cord, Matthijs Douze, Francisco Massa, Alexandre Sablayrolles, and Hervé Jégou · 2021
Closest in time.
Medical transformer: Gated axial-attention for medical image segmentation
Jeya Maria Jose Valanarasu, Poojan Oza, Ilker Hacihaliloglu, and Vishal M Patel · 2021
Closest in time.
Transbts: Multimodal brain tumor segmentation using transformer
Wenxuan Wang, Chen Chen, Meng Ding, Jiangyun Li, Hong Yu, and Sen Zha · 2021
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Pyramid vision transformer: A versatile backbone for dense prediction without convolutions
Wenhai Wang, Enze Xie, Xiang Li, Deng-Ping Fan, Kaitao Song, Ding Liang, Tong Lu, Ping Luo, and Ling Shao · 2021
Closest in time.
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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Co-scale conv-attentional image transformers
Weijian Xu, Yifan Xu, Tyler Chang, and Zhuowen Tu · 2021
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Transfuse: Fusing transformers and cnns for medical image segmentation
Yundong Zhang, Huiye Liu, and Qiang Hu · 2021
Closest in time.