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Accurate medical image segmentation demands the integration of multi-scale information, spanning from local features to global dependencies.
Deeply-supervised nets
Chen-Yu Lee, Saining Xie, Patrick Gallagher, Zhengyou Zhang, and Zhuowen Tu · 1938
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U-net: Convolutional networks for biomedical image segmentation
Olaf Ronneberger, Philipp Fischer, and Thomas Brox · 2015
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A computational atlas of the hippocampal formation using ex vivo, ultra-high resolution mri: Application to adaptive segmentation of in vivo mri
Juan Eugenio Iglesias, Jean C. Augustinack, Khoa Nguyen, Christopher M. Player, Allison Player, Michelle Wright, Nicole Roy, Matthew P. Frosch, Ann C. McKee, Lawrence L. Wald, Bruce Fischl, and Koen Van Leemput · 2015
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Understanding the effective receptive field in deep convolutional neural networks
Wenjie Luo, Yujia Li, Raquel Urtasun, and Richard Zemel · 2016
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On the effect of inter-observer variability for a reliable estimation of uncertainty of medical image segmentation
Alain Jungo, Raphael Meier, Ekin Ermis, Marcela Blatti-Moreno, Evelyn Herrmann, Roland Wiest, and Mauricio Reyes · 2018
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Automatic pulmonary lobe segmentation using deep learning
Hao Tang, Chupeng Zhang, and Xiaohui Xie · 2019
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Inter-observer variability of manual contour delineation of structures in ct
Leo Joskowicz, D Cohen, N Caplan, and Jacob Sosna · 2019
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Clinically applicable deep learning framework for organs at risk delineation in ct images
Hao Tang, Xuming Chen, Yang Liu, Zhipeng Lu, Junhua You, Mingzhou Yang, Shengyu Yao, Guoqi Zhao, Yi Xu, Tingfeng Chen, et al · 2019
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Fully convolutional multi-scale residual densenets for cardiac segmentation and automated cardiac diagnosis using ensemble of classifiers
Mahendra Khened, Varghese Alex Kollerathu, and Ganapathy Krishnamurthi · 2019
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3d mri brain tumor segmentation using autoencoder regularization
Andriy Myronenko · 2019
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2017 robotic instrument segmentation challenge
Max Allan, Alex Shvets, Thomas Kurmann, Zichen Zhang, Rahul Duggal, Yun-Hsuan Su, Nicola Rieke, Iro Laina, Niveditha Kalavakonda, Sebastian Bodenstedt, et al · 2019
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A population-based phenome-wide association study of cardiac and aortic structure and function
Wenjia Bai, Hideaki Suzuki, Jian Huang, Catherine Francis, Shuo Wang, Giacomo Tarroni, Florian Guitton, Nay Aung, Kenneth Fung, Steffen E Petersen, et al · 2020
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Artificial intelligence–enabled rapid diagnosis of patients with covid-19
Xueyan Mei, Hao-Chih Lee, Kai-yue Diao, Mingqian Huang, Bin Lin, Chenyu Liu, Zongyu Xie, Yixuan Ma, Philip M Robson, Michael Chung, et al · 2020
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Aunet: attention-guided dense-upsampling networks for breast mass segmentation in whole mammograms
Hui Sun, Cheng Li, Boqiang Liu, Zaiyi Liu, Meiyun Wang, Hairong Zheng, David Dagan Feng, and Shanshan Wang · 2020
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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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Multi-scale self-guided attention for medical image segmentation
Ashish Sinha and Jose Dolz · 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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Efficiently modeling long sequences with structured state spaces
Albert Gu, Karan Goel, and Christopher Re · 2021
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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 · 2022
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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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nnformer: Volumetric medical image segmentation via a 3d transformer
Hong-Yu Zhou, Jiansen Guo, Yinghao Zhang, Xiaoguang Han, Lequan Yu, Liansheng Wang, and Yizhou Yu · 2023
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Global context vision transformers
Ali Hatamizadeh, Hongxu Yin, Greg Heinrich, Jan Kautz, and Pavlo Molchanov · 2023
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Mamba: Linear-time sequence modeling with selective state spaces
Albert Gu and Tri Dao · 2023
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Albert Gu, Isys Johnson, Karan Goel, Khaled Saab, Tri Dao, Atri Rudra, and Christopher Ré · 2021
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Annotation-efficient deep learning for automatic medical image segmentation
Shanshan Wang, Cheng Li, Rongpin Wang, Zaiyi Liu, Meiyun Wang, Hongna Tan, Yaping Wu, Xinfeng Liu, Hui Sun, Rui Yang, et al · 2021
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D-unet: A dimension-fusion u shape network for chronic stroke lesion segmentation
Yongjin Zhou, Weijian Huang, Pei Dong, Yong Xia, and Shanshan Wang · 2021
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Swin unetr: Swin transformers for semantic segmentation of brain tumors in mri images
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UNet-2022: Exploring dynamics in non-isomorphic architecture
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Tianyang Lin, Yuxin Wang, Xiangyang Liu, and Xipeng Qiu · 2022
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Neuropathologist-level integrated classification of adult-type diffuse gliomas using deep learning from whole-slide pathological images
Weiwei Wang, Yuanshen Zhao, Lianghong Teng, Jing Yan, Yang Guo, Yuning Qiu, Yuchen Ji, Bin Yu, Dongling Pei, Wenchao Duan, et al · 2023
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Jun Ma, Yao Zhang, Song Gu, Cheng Ge, Shihao Ma, Adamo Young, Cheng Zhu, Kangkang Meng, Xin Yang, Ziyan Huang, et al · 2023
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The multi-modality cell segmentation challenge: towards universal solutions
Jun Ma, Ronald Xie, Shamini Ayyadhury, Cheng Ge, Anubha Gupta, Ritu Gupta, Song Gu, Yao Zhang, Gihun Lee, Joonkee Kim, et al · 2023
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U-mamba: Enhancing long-range dependency for biomedical image segmentation
Jun Ma, Feifei Li, and Bo Wang · 2024
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Vmamba: Visual state space model
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Segmamba: Long-range sequential modeling mamba for 3d medical image segmentation
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