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Self-supervised pretraining followed by supervised fine-tuning has seen success in image recognition, especially when labeled examples are scarce, but has received limited attention in medical image analysis.
Self-organizing neural network that discovers surfaces in random-dot stereograms
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Unsupervised visual representation learning by context prediction
Carl Doersch, Abhinav Gupta, and Alexei A Efros · 2015
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Deep residual learning for image recognition
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Mehdi Noroozi and Paolo Favaro · 2016
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Colorful image colorization
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Knowledge transfer for melanoma screening with deep learning
Afonso Menegola, Michel Fornaciali, Ramon Pires, Flávia Vasques Bittencourt, Sandra Avila, and Eduardo Valle · 2017
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Chestx-ray8: Hospital-scale chest x-ray database and benchmarks on weakly-supervised classification and localization of common thorax diseases
Xiaosong Wang, Yifan Peng, Le Lu, Zhiyong Lu, Mohammadhadi Bagheri, and Ronald M Summers · 2017
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Large batch training of convolutional networks
Yang You, Igor Gitman, and Boris Ginsburg · 2017
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Unsupervised representation learning by predicting image rotations
Spyros Gidaris, Praveer Singh, and Nikos Komodakis · 2018
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Dhruv Mahajan, Ross Girshick, Vignesh Ramanathan, Kaiming He, Manohar Paluri, Yixuan Li, Ashwin Bharambe, and Laurens van der Maaten · 2018
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Aaron van den Oord, Yazhe Li, and Oriol Vinyals · 2018
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Pierre Sermanet, Corey Lynch, Yevgen Chebotar, Jasmine Hsu, Eric Jang, Stefan Schaal, and Sergey Levine · 2018
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Hannah Spitzer, Kai Kiwitz, Katrin Amunts, Stefan Harmeling, and Timo Dickscheid · 2018
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Unsupervised feature learning via non-parametric instance discrimination
Zhirong Wu, Yuanjun Xiong, Stella X Yu, and Dahua Lin · 2018
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Learning representations by maximizing mutual information across views
Philip Bachman, R Devon Hjelm, and William Buchwalter · 2019
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Self-supervised learning for cardiac MR image segmentation by anatomical position prediction
Wenjia Bai, Chen Chen, Giacomo Tarroni, Jinming Duan, Florian Guitton, Steffen E Petersen, Yike Guo, Paul M Matthews, and Daniel Rueckert · 2019
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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, 2019
Sihong Chen, Kai Ma, and Yefeng Zheng · 2019
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Not-so-supervised: a survey of semi-supervised, multi-instance, and transfer learning in medical image analysis
Veronika Cheplygina, Marleen de Bruijne, and Josien PW Pluim · 2019
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Transfer learning by adaptive merging of multiple models
Robin Geyer, Luca Corinzia, and Viktor Wegmayr · 2019
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Visualizing and interpreting feature reuse of pretrained cnns for histopathology
Mara Graziani, Vincent Andrearczyk, and Henning Müller · 2019
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Momentum contrast for unsupervised visual representation learning
Kaiming He, Haoqi Fan, Yuxin Wu, Saining Xie, and Ross Girshick · 2019
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Data-efficient image recognition with contrastive predictive coding
Olivier J Hénaff, Aravind Srinivas, Jeffrey De Fauw, Ali Razavi, Carl Doersch, SM Eslami, and Aaron van den Oord · 2019
Improved baselines with momentum contrastive learning
Xinlei Chen, Haoqi Fan, Ross Girshick, and Kaiming He · 2020
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Momentum contrast for unsupervised visual representation learning
Kaiming He, Haoqi Fan, Yuxin Wu, Saining Xie, and Ross Girshick · 2020
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Sample-efficient deep learning for COVID-19 diagnosis based on CT scans
Xuehai He, Xingyi Yang, Shanghang Zhang, Jinyu Zhao, Yichen Zhang, Eric Xing, and Pengtao Xie · 2020
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Joint liver lesion segmentation and classification via transfer learning
Michal Heker and Hayit Greenspan · 2020
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A transfer learning method with deep residual network for pediatric pneumonia diagnosis
Gaobo Liang and Lixin Zheng · 2020
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Learning deep representations by mutual information estimation and maximization
R Devon Hjelm, Alex Fedorov, Samuel Lavoie-Marchildon, Karan Grewal, Phil Bachman, Adam Trischler, and Yoshua Bengio · 2019
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Chexpert: A large chest radiograph dataset with uncertainty labels and expert comparison
Jeremy Irvin, Pranav Rajpurkar, Michael Ko, Yifan Yu, Silviana Ciurea-Ilcus, Chris Chute, Henrik Marklund, Behzad Haghgoo, Robyn Ball, Katie Shpanskaya, et al · 2019
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Big transfer (BiT): General visual representation learning
Alexander Kolesnikov, Lucas Beyer, Xiaohua Zhai, Joan Puigcerver, Jessica Yung, Sylvain Gelly, and Neil Houlsby · 2019
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Align, attend and locate: Chest x-ray diagnosis via contrast induced attention network with limited supervision
Jingyu Liu, Gangming Zhao, Yu Fei, Ming Zhang, Yizhou Wang, and Yizhou Yu · 2019
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Transfusion: Understanding transfer learning for medical imaging
Maithra Raghu, Chiyuan Zhang, Jon Kleinberg, and Samy Bengio · 2019
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Yonglong Tian, Dilip Krishnan, and Phillip Isola · 2019
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Dual network architecture for few-view CT-trained on imagenet data and transferred for medical imaging
Huidong Xie, Hongming Shan, Wenxiang Cong, Xiaohua Zhang, Shaohua Liu, Ruola Ning, and Ge Wang · 2019
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Semi-supervised medical image classification with relation-driven self-ensembling model
Quande Liu, Lequan Yu, Luyang Luo, Qi Dou, and Pheng Ann Heng · 2020
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A deep learning system for differential diagnosis of skin diseases
Yuan Liu, Ayush Jain, Clara Eng, David H Way, Kang Lee, Peggy Bui, Kimberly Kanada, Guilherme de Oliveira Marinho, Jessica Gallegos, Sara Gabriele, et al · 2020
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International evaluation of an AI system for breast cancer screening
Scott Mayer McKinney, Marcin Sieniek, Varun Godbole, Jonathan Godwin, Natasha Antropova, Hutan Ashrafian, Trevor Back, Mary Chesus, Greg C Corrado, Ara Darzi, et al · 2020
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Self-supervised learning of pretext-invariant representations
Ishan Misra and Laurens van der Maaten · 2020
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What is being transferred in transfer learning?
Behnam Neyshabur, Hanie Sedghi, and Chiyuan Zhang · 2020
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Moco pretraining improves representation and transferability of chest X-ray models
Hari Sowrirajan, Jingbo Yang, Andrew Y Ng, and Pranav Rajpurkar · 2020
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Self-supervised learning of video-induced visual invariances
Michael Tschannen, Josip Djolonga, Marvin Ritter, Aravindh Mahendran, Neil Houlsby, Sylvain Gelly, and Mario Lucic · 2020
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Focalmix: Semi-supervised learning for 3d medical image detection
Dong Wang, Yuan Zhang, Kexin Zhang, and Liwei Wang · 2020
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Contrastive learning of medical visual representations from paired images and text
Yuhao Zhang, Hang Jiang, Yasuhide Miura, Christopher D Manning, and Curtis P Langlotz · 2020
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Comparing to learn: Surpassing imagenet pretraining on radiographs by comparing image representations
Hong-Yu Zhou, Shuang Yu, Cheng Bian, Yifan Hu, Kai Ma, and Yefeng Zheng · 2020
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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
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Imbalance-aware self-supervised learning for 3d radiomic representations
Hongwei Li, Fei-Fei Xue, Krishna Chaitanya, Shengda Liu, Ivan Ezhov, Benedikt Wiestler, Jianguo Zhang, and Bjoern Menze · 2021
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