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Transformers have made remarkable progress towards modeling long-range dependencies within the medical image analysis domain.
Acceleration of stochastic approximation by averaging
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Generative adversarial nets
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Spatial transformer networks
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U-net: Convolutional networks for biomedical image segmentation
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Semantic segmentation using adversarial networks
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Improved techniques for training gans
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Guest editorial deep learning in medical imaging: Overview and future promise of an exciting new technique
Hayit Greenspan, Bram Van Ginneken, and Ronald M Summers · 2016
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Rethinking the inception architecture for computer vision
Christian Szegedy, Vincent Vanhoucke, Sergey Ioffe, Jonathon Shlens, and Zbigniew Wojna · 2016
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Review of liver segmentation and computer assisted detection/diagnosis methods in computed tomography
Mehrdad Moghbel, Syamsiah Mashohor, Rozi Mahmud, and M Iqbal Bin Saripan · 2017
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A survey on deep learning in medical image analysis
Geert Litjens, Thijs Kooi, Babak Ehteshami Bejnordi, Arnaud Arindra Adiyoso Setio, Francesco Ciompi, Mohsen Ghafoorian, Jeroen AWM van der Laak, Bram Van Ginneken, and Clara I Sánchez · 2017
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Deep adversarial networks for biomedical image segmentation utilizing unannotated images
Yizhe Zhang, Lin Yang, Jianxu Chen, Maridel Fredericksen, David P Hughes, and Danny Z Chen · 2017
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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
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Mask r-cnn
Kaiming He, Georgia Gkioxari, Piotr Dollár, and Ross Girshick · 2017
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Large kernel matters–improve semantic segmentation by global convolutional network
Chao Peng, Xiangyu Zhang, Gang Yu, Guiming Luo, and Jian Sun · 2017
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Focal loss for dense object detection
Tsung-Yi Lin, Priya Goyal, Ross Girshick, Kaiming He, and Piotr Dollár · 2017
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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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Wasserstein generative adversarial networks
Martin Arjovsky, Soumith Chintala, and Léon Bottou · 2017
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Improved training of wasserstein gans
Ishaan Gulrajani, Faruk Ahmed, Martin Arjovsky, Vincent Dumoulin, and Aaron Courville · 2017
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Are gans created equal? a large-scale study
Mario Lucic, Karol Kurach, Marcin Michalski, Sylvain Gelly, and Olivier Bousquet · 2017
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Least squares generative adversarial networks
Xudong Mao, Qing Li, Haoran Xie, Raymond YK Lau, Zhen Wang, and Stephen Paul Smolley · 2017
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Deformable convolutional networks
Jifeng Dai, Haozhi Qi, Yuwen Xiong, Yi Li, Guodong Zhang, Han Hu, and Yichen Wei · 2017
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Computational radiomics system to decode the radiographic phenotype
Joost JM Van Griethuysen, Andriy Fedorov, Chintan Parmar, Ahmed Hosny, Nicole Aucoin, Vivek Narayan, Regina GH Beets-Tan, Jean-Christophe Fillion-Robin, Steve Pieper, and Hugo JWL Aerts · 2017
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Segan: Adversarial network with multi-scale l 1 loss for medical image segmentation
Yuan Xue, Tao Xu, Han Zhang, L Rodney Long, and Xiaolei Huang · 2018
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Super-resolution mri through deep learning
Qing Lyu, Chenyu You, Hongming Shan, and Ge Wang · 2018
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Structurally-sensitive multi-scale deep neural network for low-dose ct denoising
Chenyu You, Qingsong Yang, Hongming Shan, Lars Gjesteby, Guang Li, Shenghong Ju, Zhuiyang Zhang, Zhen Zhao, Yi Zhang, Wenxiang Cong, et al · 2018
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Semi-supervised skin lesion segmentation via transformation consistent self-ensembling model
Xiaomeng Li, Lequan Yu, Hao Chen, Chi-Wing Fu, and Pheng-Ann Heng · 2018
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Asdnet: Attention based semi-supervised deep networks for medical image segmentation
Dong Nie, Yaozong Gao, Li Wang, and Dinggang Shen · 2018
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Multiscale network followed network model for retinal vessel segmentation
Yicheng Wu, Yong Xia, Yang Song, Yanning Zhang, and Weidong Cai · 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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mixup: Beyond empirical risk minimization
Hongyi Zhang, Moustapha Cisse, Yann N Dauphin, and David Lopez-Paz · 2018
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Super-resolution mri and ct through gan-circle
Qing Lyu, Chenyu You, Hongming Shan, Yi Zhang, and Ge Wang · 2019
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Low-dose ct via deep cnn with skip connection and network-in-network
Chenyu You, Linfeng Yang, Yi Zhang, and Ge Wang · 2019
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Self-attention generative adversarial networks
Han Zhang, Ian Goodfellow, Dimitris Metaxas, and Augustus Odena · 2019
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Uncertainty-aware self-ensembling model for semi-supervised 3d left atrium segmentation
Lequan Yu, Shujun Wang, Xiaomeng Li, Chi-Wing Fu, and Pheng-Ann Heng · 2019
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Joint learning of saliency detection and weakly supervised semantic segmentation
Yu Zeng, Yunzhi Zhuge, Huchuan Lu, and Lihe Zhang · 2019
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Semi-supervised medical image segmentation via learning consistency under transformations
Gerda Bortsova, Florian Dubost, Laurens Hogeweg, Ioannis Katramados, and Marleen de Bruijne · 2019
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Vessel-net: retinal vessel segmentation under multi-path supervision
Yicheng Wu, Yong Xia, Yang Song, Donghao Zhang, Dongnan Liu, Chaoyi Zhang, and Weidong Cai · 2019
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CT super-resolution GAN constrained by the identical, residual, and cycle learning ensemble (gan-circle)
Chenyu You, Guang Li, Yi Zhang, Xiaoliu Zhang, Hongming Shan, Mengzhou Li, Shenghong Ju, Zhen Zhao, Zhuiyang Zhang, Wenxiang Cong, et al · 2019
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Decoupled weight decay regularization
Ilya Loshchilov and Frank Hutter · 2019
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Dayang Wang, Zhan Wu, and Hengyong Yu · 2021
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Taming transformers for high-resolution image synthesis
Patrick Esser, Robin Rombach, and Bjorn Ommer · 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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Going deeper with image transformers
Hugo Touvron, Matthieu Cord, Alexandre Sablayrolles, Gabriel Synnaeve, and Hervé Jégou · 2021
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A review of deep learning in medical imaging: Imaging traits, technology trends, case studies with progress highlights, and future promises
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Attention gated networks: Learning to leverage salient regions in medical images
Jo Schlemper, Ozan Oktay, Michiel Schaap, Mattias Heinrich, Bernhard Kainz, Ben Glocker, and Daniel Rueckert · 2019
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Cutmix: Regularization strategy to train strong classifiers with localizable features
Sangdoo Yun, Dongyoon Han, Seong Joon Oh, Sanghyuk Chun, Junsuk Choe, and Youngjoon Yoo · 2019
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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
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Deep learning based high-resolution reconstruction of trabecular bone microstructures from low-resolution ct scans using gan-circle
Indranil Guha, Syed Ahmed Nadeem, Chenyu You, Xiaoliu Zhang, Steven M Levy, Ge Wang, James C Torner, and Punam K Saha · 2020
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End-to-end object detection with transformers
Nicolas Carion, Francisco Massa, Gabriel Synnaeve, Nicolas Usunier, Alexander Kirillov, and Sergey Zagoruyko · 2020
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Learning joint spatial-temporal transformations for video inpainting
Yanhong Zeng, Jianlong Fu, and Hongyang Chao · 2020
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Training data-efficient image transformers & distillation through attention
Hugo Touvron, Matthieu Cord, Matthijs Douze, Francisco Massa, Alexandre Sablayrolles, and Hervé Jégou · 2020
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S. Kevin Zhou, Hayit Greenspan, Christos Davatzikos, James S. Duncan, Bram Van Ginneken, Anant Madabhushi, Jerry L. Prince, Daniel Rueckert, and Ronald M. Summers · 2021
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Marginal loss and exclusion loss for partially supervised multi-organ segmentation
Gonglei Shi, Li Xiao, Yang Chen, and S Kevin Zhou · 2021
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Label-free segmentation of covid-19 lesions in lung ct
Qingsong Yao, Li Xiao, Peihang Liu, and S Kevin Zhou · 2021
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Vision transformer with progressive sampling
Xiaoyu Yue, Shuyang Sun, Zhanghui Kuang, Meng Wei, Philip Torr, Wayne Zhang, and Dahua Lin · 2021
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Chenyu You, Ruihan Zhao, Lawrence Staib, and James S Duncan · 2021
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Boundary loss-based 2.5 d fully convolutional neural networks approach for segmentation: A case study of the liver and tumor on computed tomography
Yuexing Han, Xiaolong Li, Bing Wang, and Lu Wang · 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 Roth, 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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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
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Transgan: Two transformers can make one strong gan
Yifan Jiang, Shiyu Chang, and Zhangyang Wang · 2021
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Sketchedit: Mask-free local image manipulation with partial sketches
Yu Zeng, Zhe Lin, and Vishal M Patel · 2021
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Cr-fill: Generative image inpainting with auxiliary contextual reconstruction
Yu Zeng, Zhe Lin, Huchuan Lu, and Vishal M Patel · 2021
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Compositional transformers for scene generation
Dor Arad Hudson and Larry Zitnick · 2021
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Improved transformer for high-resolution gans
Long Zhao, Zizhao Zhang, Ting Chen, Dimitris N Metaxas, and Han Zhang · 2021
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Improving visual quality of image synthesis by a token-based generator with transformers
Yanhong Zeng, Huan Yang, Hongyang Chao, Jianbo Wang, and Jianlong Fu · 2021
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Coatnet: Marrying convolution and attention for all data sizes
Zihang Dai, Hanxiao Liu, Quoc V Le, and Mingxing Tan · 2021
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Early convolutions help transformers see better
Tete Xiao, Piotr Dollar, Mannat Singh, Eric Mintun, Trevor Darrell, and Ross Girshick · 2021
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Segformer: Simple and efficient design for semantic segmentation with transformers
Enze Xie, Wenhai Wang, Zhiding Yu, Anima Anandkumar, Jose M Alvarez, and Ping Luo · 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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A new approach for computer-aided detection of coronavirus (covid-19) from ct and x-ray images using machine learning methods
Ahmet Saygılı · 2021
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Large-scale screening to distinguish between covid-19 and community-acquired pneumonia using infection size-aware classification
Feng Shi, Liming Xia, Fei Shan, Bin Song, Dijia Wu, Ying Wei, Huan Yuan, Huiting Jiang, Yichu He, Yaozong Gao, et al · 2021
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Masked autoencoders are scalable vision learners
Kaiming He, Xinlei Chen, Saining Xie, Yanghao Li, Piotr Dollár, and Ross Girshick · 2022
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Mirnf: Medical image registration via neural fields
Shanlin Sun, Kun Han, Deying Kong, Chenyu You, and Xiaohui Xie · 2022
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Learning correspondences of cardiac motion from images using biomechanics-informed modeling
Xiaoran Zhang, Chenyu You, Shawn Ahn, Juntang Zhuang, Lawrence Staib, and James Duncan · 2022
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Simcvd: Simple contrastive voxel-wise representation distillation for semi-supervised medical image segmentation
Chenyu You, Yuan Zhou, Ruihan Zhao, Lawrence Staib, and James S Duncan · 2022
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Incremental learning meets transfer learning: Application to multi-site prostate mri segmentation
Chenyu You, Jinlin Xiang, Kun Su, Xiaoran Zhang, Siyuan Dong, John Onofrey, Lawrence Staib, and James S Duncan · 2022
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Chenyu You, Weicheng Dai, Lawrence Staib, and James S Duncan · 2022
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Mine your own anatomy: Revisiting medical image segmentation with extremely limited labels
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Joint liver and hepatic lesion segmentation using a hybrid cnn with transformer layers
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Unetr: Transformers for 3d medical image segmentation
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Vision transformer with deformable attention
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