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Transfer learning has become a standard practice to mitigate the lack of labeled data in medical classification tasks.
Transfusion: Understanding Transfer Learning for Medical Imaging
Maithra Raghu, Chiyuan Zhang, Jon Kleinberg, and Samy Bengio · 1902
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Unsupervised Learning of Visual Features by Contrasting Cluster Assignments
Mathilde Caron, Ishan Misra, Julien Mairal, Priya Goyal, Piotr Bojanowski, and Armand Joulin · 2006
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Bootstrap your own latent: A new approach to self-supervised Learning
Jean-Bastien Grill, Florian Strub, Florent Altché, Corentin Tallec, Pierre H. Richemond, Elena Buchatskaya, Carl Doersch, Bernardo Avila Pires, Zhaohan Daniel Guo, Mohammad Gheshlaghi Azar, Bilal Piot, Koray Kavukcuoglu, Rémi Munos, and Michal Valko · 2006
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3D Self-Supervised Methods for Medical Imaging
Aiham Taleb, Winfried Loetzsch, Noel Danz, Julius Severin, Thomas Gaertner, Benjamin Bergner, and Christoph Lippert · 2006
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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, Jakob Uszkoreit, and Neil Houlsby · 2010
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Two public chest x-ray datasets for computer-aided screening of pulmonary diseases
Stefan Jaeger, Sema Candemir, Sameer Antani, Yì-Xiáng J. Wáng, Pu-Xuan Lu, and George Thoma · 2014
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Discriminative Unsupervised Feature Learning with Exemplar Convolutional Neural Networks
Alexey Dosovitskiy, Philipp Fischer, Jost Tobias Springenberg, Martin Riedmiller, and Thomas Brox · 2015
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Going deeper with convolutions
Christian Szegedy, Wei Liu, Yangqing Jia, Pierre Sermanet, Scott Reed, Dragomir Anguelov, Dumitru Erhan, Vincent Vanhoucke, and Andrew Rabinovich · 2015
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Transfer Learning for Cell Nuclei Classification in Histopathology Images
Neslihan Bayramoglu and Janne Heikkilä · 2016
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Diagnostic Assessment of Deep Learning Algorithms for Detection of Lymph Node Metastases in Women With Breast Cancer
Babak Ehteshami Bejnordi, Mitko Veta, Paul Johannes van Diest, Bram van Ginneken, Nico Karssemeijer, Geert Litjens, Jeroen A. W. M. van der Laak, , and the CAMELYON16 Consortium · 2017
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Densely connected convolutional networks
Gao Huang, Zhuang Liu, Laurens Van Der Maaten, and Kilian Q Weinberger · 2017
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Deep Clustering for Unsupervised Learning of Visual Features
Mathilde Caron, Piotr Bojanowski, Armand Joulin, and Matthijs Douze · 2018
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Identifying medical diagnoses and treatable diseases by image-based deep learning
Daniel S. Kermany, Michael Goldbaum, Wenjia Cai, Carolina C.S. Valentim, Huiying Liang, Sally L. Baxter, Alex McKeown, Ge Yang, Xiaokang Wu, Fangbing Yan, Justin Dong, Made K. Prasadha, Jacqueline Pei, Magdalene Y.L. Ting, Jie Zhu, Christina Li, Sierra Hewett, Jason Dong, Ian Ziyar, Alexander Shi, Runze Zhang, Lianghong Zheng, Rui Hou, William Shi, Xin Fu, Yaou Duan, Viet A.N. Huu, Cindy Wen, Edward D. Zhang, Charlotte L. Zhang, Oulan Li, Xiaobo Wang, Michael A. Singer, Xiaodong Sun, Jie Xu, Ali Tafreshi, M. Anthony Lewis, Huimin Xia, and Kang Zhang · 2018
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Dynamic Meta-Embeddings for Improved Sentence Representations
Douwe Kiela, Changhan Wang, and Kyunghyun Cho · 2018
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Automated detection of diabetic retinopathy using deep learning
Carson Lam, Darvin Yi, Margaret Guo, and Tony Lindsey · 2018
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Transfer learning from chest x-ray pre-trained convolutional neural network for learning mammogram data
Bens Pardamean, Tjeng Wawan Cenggoro, Reza Rahutomo, Arif Budiarto, and Ettikan Kandasamy Karuppiah · 2018
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Rotation equivariant cnns for digital pathology
Bastiaan S Veeling, Jasper Linmans, Jim Winkens, Taco Cohen, and Max Welling · 2018
Cited alongside, same era.
Glioma grading on conventional mr images: a deep learning study with transfer learning
Yang Yang, Lin-Feng Yan, Xin Zhang, Yu Han, Hai-Yan Nan, Yu-Chuan Hu, Bo Hu, Song-Lin Yan, Jin Zhang, Dong-Liang Cheng, et al · 2018
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Divide-and-Rule: Self-Supervised Learning for Survival Analysis in Colorectal Cancer
Christian Abbet, Inti Zlobec, Behzad Bozorgtabar, and Jean-Philippe Thiran · 2020
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A simple framework for contrastive learning of visual representations
Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey Hinton · 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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Contrastive Representation Learning: A Framework and Review
Phuc H. Le-Khac, Graham Healy, and Alan F. Smeaton · 2020
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Chest radiograph interpretation with deep learning models: Assessment with radiologist-adjudicated reference standards and population-adjusted evaluation
Anna Majkowska, Sid Mittal, David F. Steiner, Joshua J. Reicher, Scott Mayer McKinney, Gavin E. Duggan, Krish Eswaran, Po-Hsuan Cameron Chen, Yun Liu, Sreenivasa Raju Kalidindi, Alexander Ding, Greg S. Corrado, Daniel Tse, and Shravya Shetty · 2020
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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
Cited alongside, same era.
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
Cited alongside, same era.
Rethinking ImageNet Pre-Training
Kaiming He, Ross Girshick, and Piotr Dollar · 2019
Cited alongside, same era.
Chexpert: A large chest radiograph dataset with uncertainty labels and expert comparison, 2019
Jeremy Irvin, Pranav Rajpurkar, Michael Ko, Yifan Yu, Silviana Ciurea-Ilcus, Chris Chute, Henrik Marklund, Behzad Haghgoo, Robyn Ball, Katie Shpanskaya, Jayne Seekins, David A. Mong, Safwan S. Halabi, Jesse K. Sandberg, Ricky Jones, David B. Larson, Curtis P. Langlotz, Bhavik N. Patel, Matthew P. Lungren, and Andrew Y. Ng · 2019
Cited alongside, same era.
Modeling Disease Progression in Retinal OCTs with Longitudinal Self-supervised Learning
Antoine Rivail, Ursula Schmidt-Erfurth, Wolf-Dieter Vogl, Sebastian M. Waldstein, Sophie Riedl, Christoph Grechenig, Zhichao Wu, and Hrvoje Bogunovic · 2019
Cited alongside, same era.
EfficientNet: Rethinking model scaling for convolutional neural networks
Mingxing Tan and Quoc Le · 2019
Cited alongside, same era.
Models genesis: Generic autodidactic models for 3d medical image analysis
Zongwei Zhou, Vatsal Sodha, Md Mahfuzur Rahman Siddiquee, Ruibin Feng, Nima Tajbakhsh, Michael B Gotway, and Jianming Liang · 2019
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How Useful Is Self-Supervised Pretraining for Visual Tasks?
Alejandro Newell and Jia Deng · 2020
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Big Self-Supervised Models Advance Medical Image Classification
Shekoofeh Azizi, Basil Mustafa, Fiona Ryan, Zachary Beaver, Jan Freyberg, Jonathan Deaton, Aaron Loh, Alan Karthikesalingam, Simon Kornblith, Ting Chen, Vivek Natarajan, and Mohammad Norouzi · 2021
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An Evaluation of Self-Supervised Pre-Training for Skin-Lesion Analysis
Levy Chaves, Alceu Bissoto, Eduardo Valle, and Sandra Avila · 2021
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Jonas Dippel, Steffen Vogler, and Johannes Höhne · 2021
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CheXtransfer: Performance and Parameter Efficiency of ImageNet Models for Chest X-Ray Interpretation
Alexander Ke, William Ellsworth, Oishi Banerjee, Andrew Y. Ng, and Pranav Rajpurkar · 2021
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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 · 2021
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Multimodal self-supervised learning for medical image analysis
Aiham Taleb, Christoph Lippert, Tassilo Klein, and Moin Nabi · 2021
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Causality matters in medical imaging
Daniel C. Castro, Ian Walker, and Ben Glocker · 2041
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