Fetching the paper…
Reading the bibliography…
Pretraining with large-scale 3D volumes has a potential for improving the segmentation performance on a target medical image dataset where the training images and annotations are limited.
U-Net: Convolutional networks for biomedical image segmentation
Olaf Ronneberger, Philipp Fischer, and Thomas Brox · 2015
Earlier work this paper cites.
3D U-Net: Learning dense volumetric segmentation from sparse annotation
Ahmed Abdulkadir, Soeren S. Lienkamp, Thomas Brox, and Olaf Ronneberger · 2016
Earlier work this paper cites.
Deep Convolutional Neural Networks for Computer-Aided Detection : CNN Architectures , Dataset Characteristics and Transfer Learning
Le Lu, Hoo-chang Shin, Holger R Roth, Mingchen Gao, Le Lu, Senior Member, Ziyue Xu, Isabella Nogues, Jianhua Yao, Daniel Mollura, and Ronald M Summers · 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
Earlier work this paper cites.
Unsupervised Learning of Visual Representations by Solving Jigsaw Puzzles
Mehdi Noroozi and Paolo Favaro · 2016
Earlier work this paper cites.
Context Encoders: Feature Learning by Inpainting
Deepak Pathak, Philipp Krähenbühl, Jeff Donahue, Trevor Darrell, and Alexei A. Efros · 2016
Earlier work this paper cites.
Colorful Image Colorization
Richard Zhang, Phillip Isola, and Alexei A. Efros · 2016
Earlier work this paper cites.
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.
Evaluation of segmentation methods on head and neck CT: Auto-segmentation challenge 2015
Patrik F. Raudaschl, Paolo Zaffino, Gregory C. Sharp, Maria Francesca Spadea, Antong Chen, Benoit M. Dawant, Thomas Albrecht, Tobias Gass, Christoph Langguth, Marcel Luthi, Florian Jung, Oliver Knapp, Stefan Wesarg, Richard Mannion-Haworth, Mike Bowes, Annaliese Ashman, Gwenael Guillard, Alan Brett, Graham Vincent, Mauricio Orbes-Arteaga, David Cardenas-Pena, German Castellanos-Dominguez, Nava Aghdasi, Yangming Li, Angelique Berens, Kris Moe, Blake Hannaford, Rainer Schubert, and Karl D. Fritscher · 2017
Earlier work this paper cites.
Deep learning in medical image analysis
Dinggang Shen, Guorong Wu, and Heung-Il Suk · 2017
Earlier work this paper cites.
Unsupervised representation learning by predicting image rotations
Spyros Gidaris, Praveer Singh, and Nikos Komodakis · 2018
Earlier work this paper cites.
Unet++: A nested u-net architecture for medical image segmentation
Zongwei Zhou, Md Mahfuzur Rahman Siddiquee, Nima Tajbakhsh, and Jianming Liang · 2018
Earlier work this paper cites.
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
Earlier work this paper cites.
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
Earlier work this paper cites.
Transfusion: Understanding transfer learning for medical imaging
Maithra Raghu, Chiyuan Zhang, Jon Kleinberg, and Samy Bengio · 2019
Earlier work this paper cites.
Recalibrating fully convolutional networks with spatial and channel ’squeeze and excitation’ blocks
Abhijit Guha Roy, Nassir Navab, and Christian Wachinger · 2019
Earlier work this paper cites.
An artificial intelligence framework for automatic segmentation and volumetry of vestibular schwannomas from contrast-enhanced T1-weighted and high-resolution T2-weighted MRI
Jonathan Shapey, Guotai Wang, Reuben Dorent, Alexis Dimitriadis, Wenqi Li, Ian Paddick, Neil Kitchen, Sotirios Bisdas, Shakeel R. Saeed, Sebastien Ourselin, Robert Bradford, and Tom Vercauteren · 2019
Earlier work this paper cites.
Embracing imperfect datasets: A review of deep learning solutions for medical image segmentation
Nima Tajbakhsh, Laura Jeyaseelan, Qian Li, Jeffrey Chiang, Zhihao Wu, and Xiaowei Ding · 2019
Earlier work this paper cites.
A simple framework for contrastive learning of visual representations
Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey Hinton · 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.
SegTHOR: Segmentation of Thoracic Organs at Risk in CT images
Zoe Lambert, Caroline Petitjean, Bernard Dubray, and Su Kuan · 2020
Cited alongside, same era.
Rubik’s Cube+: A self-supervised feature learning framework for 3D medical image analysis
Jiuwen Zhu, Yuexiang Li, Yifan Hu, Kai Ma, S. Kevin Zhou, and Yefeng Zheng · 2020
Cited alongside, same era.
Emerging Properties in Self-Supervised Vision Transformers
Mathilde Caron, Hugo Touvron, Ishan Misra, Hervé Jegou, Julien Mairal, Piotr Bojanowski, and Armand Joulin · 2021
Cited alongside, same era.
TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation
UNETR: Transformers for 3D Medical Image Segmentation
Ali Hatamizadeh, Yucheng Tang, Vishwesh Nath, Dong Yang, Andriy Myronenko, Bennett Landman, Holger Roth, and Daguang Xu · 2022
Later among the works it cites.
Masked autoencoders are scalable vision learners
Kaiming He, Xinlei Chen, Saining Xie, Yanghao Li, Piotr Doll, and Ross Girshick · 2022
Later among the works it cites.
Fully transformer network for skin lesion analysis
Xinzi He, Ee Leng Tan, Hanwen Bi, Xuzhe Zhang, Shijie Zhao, and Baiying Lei · 2022
Later among the works it cites.
AbdomenCT-1K: Is Abdominal Organ Segmentation a Solved Problem?
Jun Ma, Yao Zhang, Song Gu, Cheng Zhu, Cheng Ge, Yichi Zhang, Xingle An, Congcong Wang, Qiyuan Wang, Xin Liu, Shucheng Cao, Qi Zhang, Shangqing Liu, Yunpeng Wang, Yuhui Li, Jian He, and Xiaoping Yang · 2022
Later among the works it cites.
UNETR++: Delving into efficient and accurate 3D medical image segmentation
Abdelrahman Shaker, Muhammad Maaz, Hanoona Rasheed, Salman Khan, Ming-Hsuan Yang, and Fahad Shahbaz Khan · 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…
Jieneng Chen, Yongyi Lu, Qihang Yu, Xiangde Luo, Ehsan Adeli, Yan Wang, Le Lu, Alan L. Yuille, and Yuyin Zhou · 2021
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, Jakob Uszkoreit, and Neil Houlsby · 2021
Cited alongside, same era.
A denoising self-supervised approach for COVID-19 pneumonia lesion segmentation with limited annotated CT images
Yibo Gao, Huan Wang, Xinglong Liu, Ning Huang, Guotai Wang, and Shaoting Zhang · 2021
Cited alongside, same era.
UTNet: A Hybrid Transformer Architecture for Medical Image Segmentation
Yunhe Gao, Mu Zhou, and Dimitris N. Metaxas · 2021
Cited alongside, same era.
CA-Net: Comprehensive attention convolutional neural networks for explainable medical image segmentation
Ran Gu, Guotai Wang, Tao Song, Rui Huang, Michael Aertsen, Jan Deprest, Sébastien Ourselin, Tom Vercauteren, and Shaoting Zhang · 2021
Cited alongside, same era.
nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation
Fabian Isensee, Paul F. Jaeger, Simon A.A. Kohl, Jens Petersen, and Klaus H. Maier-Hein · 2021
Cited alongside, same era.
Self-Supervised Visual Feature Learning with Deep Neural Networks: A Survey
Longlong Jing and Yingli Tian · 2021
Cited alongside, same era.
Self-Supervised Pre-Training of Swin Transformers for 3D Medical Image Analysis
Yucheng Tang, Dong Yang, Wenqi Li, Holger R. Roth, Bennett Landman, Daguang Xu, Vishwesh Nath, and Ali Hatamizadeh · 2022
Later among the works it cites.
Semi-supervised segmentation of radiation-induced pulmonary fibrosis from lung CT scans with multi-scale guided dense attention
Guotai Wang, Shuwei Zhai, Giovanni Lasio, Baoshe Zhang, Byong Yi, Shifeng Chen, Thomas J. Macvittie, Dimitris Metaxas, Jinghao Zhou, and Shaoting Zhang · 2022
Later among the works it cites.
TotalSegmentator: robust segmentation of 104 anatomical structures in CT images
Jakob Wasserthal, Manfred Meyer, Hanns-Christian Breit, Joshy Cyriac, Shan Yang, and Martin Segeroth · 2022
Later among the works it cites.
DeSD: Self-supervised learning with deep self-distillation for 3D medical image segmentation
Yiwen Ye, Jianpeng Zhang, Ziyang Chen, and Yong Xia · 2022
Later among the works it cites.
Deep learning empowered volume delineation of whole-body organs-at-risk for accelerated radiotherapy
Wei Zhang, Qing Zhou, Jingjie Zhou, Ying Wei, Ying Shao, Yanbo Chen, and Dinggang Shen · 2022
Later among the works it cites.
nnFormer: Interleaved Transformer for Volumetric Segmentation
Hong-Yu Zhou, Jiansen Guo, Yinghao Zhang, Lequan Yu, Liansheng Wang, and Yizhou Yu · 2022
Later among the works it cites.
Masked Image Modeling Advances 3D Medical Image Analysis
Zekai Chen, Devansh Agarwal, Kshitij Aggarwal, Wiem Safta, Mariann Micsinai Balan, and Kevin Brown · 2023
Closest in time.
Transformers in medical image analysis
Kelei He, Chen Gan, Zhuoyuan Li, Islem Rekik, Zihao Yin, Wen Ji, Yang Gao, Qian Wang, Junfeng Zhang, and Dinggang Shen · 2023
Closest in time.
Ziyuan Huang, Haoyu Wang, Zongying Deng, Jin Ye, Yanzhou Su, Hui Sun, Junjun He, Yun Gu, Lixu Gu, Shaoting Zhang, and Yu Qiao · 2023
Closest in time.
CLIP-driven universal model for organ segmentation and tumor detection
Jie Liu, Yixiao Zhang, Jie-Neng Chen, Junfei Xiao, Yongyi Lu, Bennett A. Landman, Yixuan Yuan, Alan Yuille, Yucheng Tang, and Zongwei Zhou · 2023
Closest in time.
PyMIC: A deep learning toolkit for annotation-efficient medical image segmentation
Guotai Wang, Xiangde Luo, Ran Gu, Shuojue Yang, Yijie Qu, Shuwei Zhai, Qianfei Zhao, Kang Li, and Shaoting Zhang · 2023
Closest in time.
On the challenges and perspectives of foundation models for medical image analysis
Shaoting Zhang and Dimitris Metaxas · 2023
Closest in time.