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Deep learning methods for chest X-ray interpretation typically rely on pretrained models developed for ImageNet.
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 L. Ball, Katie S. 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 · 1901
Earlier work this paper cites.
Transfusion: Understanding Transfer Learning with Applications to Medical Imaging
Maithra Raghu, Chiyuan Zhang, Jon M. Kleinberg, and Samy Bengio. 2019 · 1902
Earlier work this paper cites.
Comparing Different Deep Learning Architectures for Classification of Chest Radiographs
Keno K. Bressem, Lisa Adams, Christoph Erxleben, Bernd Hamm, Stefan Niehues, and Janis Vahldiek. 2020 · 2002
Earlier work this paper cites.
Layer-wise Pruning and Auto-tuning of Layer-wise Learning Rates in Fine-tuning of Deep Networks
Youngmin Ro and Jin Young Choi. 2020 · 2002
Earlier work this paper cites.
ImageNet: A large-scale hierarchical image database. In 2009 IEEE Conference on Computer Vision and Pattern Recognition . 248–255
J. Deng, W. Dong, R. Socher, L. Li, Kai Li, and Li Fei-Fei. 2009 · 2009
Earlier work this paper cites.
MoCo Pretraining Improves Representation and Transferability of Chest X-ray Models
Hari Sowrirajan, Jingbo Yang, Andrew Y. Ng, and Pranav Rajpurkar. 2020 · 2010
Earlier work this paper cites.
Speeding up Convolutional Neural Networks with Low Rank Expansions
Max Jaderberg, Andrea Vedaldi, and Andrew Zisserman. 2014 · 2014
Earlier work this paper cites.
Distilling the Knowledge in a Neural Network
Geoffrey Hinton, Oriol Vinyals, and Jeff Dean. 2015 · 2015
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Data-free parameter pruning for Deep Neural Networks
Suraj Srinivas and R. Venkatesh Babu. 2015 · 2015
Earlier work this paper cites.
Xception: Deep Learning with Depthwise Separable Convolutions
François Chollet. 2016 · 2016
Earlier work this paper cites.
Ramprasaath R. Selvaraju, Abhishek Das, Ramakrishna Vedantam, Michael Cogswell, Devi Parikh, and Dhruv Batra. 2016 · 2016
Earlier work this paper cites.
A Survey of Model Compression and Acceleration for Deep Neural Networks
Yu Cheng, Duo Wang, Pan Zhou, and Tao Zhang. 2017 · 2017
Cited alongside, same era.
Dermatologist-level classification of skin cancer with deep neural networks
Andre Esteva, Brett Kuprel, Roberto A. Novoa, Justin Ko, Susan M. Swetter, Helen M. Blau, and Sebastian Thrun. 2017 · 2017
Cited alongside, same era.
CheXNet: Radiologist-Level Pneumonia Detection on Chest X-Rays with Deep Learning
Pranav Rajpurkar, Jeremy Irvin, Kaylie Zhu, Brandon Yang, Hershel Mehta, Tony Duan, Daisy Yi Ding, Aarti Bagul, Curtis Langlotz, Katie S. Shpanskaya, Matthew P. Lungren, and Andrew Y. Ng. 2017 · 2017
Cited alongside, same era.
Xiaosong Wang, Yifan Peng, Le Lu, Zhiyong Lu, Mohammadhadi Bagheri, and Ronald M. Summers. 2017 · 2017
Cited alongside, same era.
PyTorch: An Imperative Style, High-Performance Deep Learning Library
Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, Alban Desmaison, Andreas Kopf, Edward Yang, Zachary DeVito, Martin Raison, Alykhan Tejani, Sasank Chilamkurthy, Benoit Steiner, Lu Fang, Junjie Bai, and Soumith Chintala. 2019 · 2019
Later among the works it cites.
Covid-19: automatic detection from x-ray images utilizing transfer learning with convolutional neural networks
Ioannis D Apostolopoulos and Tzani A Mpesiana. 2020 · 2020
Later among the works it cites.
Detection of anaemia from retinal fundus images via deep learning
Akinori Mitani, Abigail Huang, Subhashini Venugopalan, Greg S. Corrado, Lily Peng, Dale R. Webster, Naama Hammel, Yun Liu, and Avinash V. Varadarajan. 2020 · 2020
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Pranav Rajpurkar, Anirudh Joshi, Anuj Pareek, Phil Chen, Amirhossein Kiani, Jeremy Irvin, Andrew Y Ng, and Matthew P Lungren. 2020a · 2020
Later among the works it cites.
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pretrainedmodels 0.7.4
Remi Cadene. 2018 · 2018
Cited alongside, same era.
Clinically applicable deep learning for diagnosis and referral in retinal disease
Jeffrey De Fauw, Joseph R. Ledsam, Bernardino Romera-Paredes, Stanislav Nikolov, Nenad Tomasev, Sam Blackwell, Harry Askham, Xavier Glorot, Brendan O’Donoghue, Daniel Visentin, George van den Driessche, Balaji Lakshminarayanan, Clemens Meyer, Faith Mackinder, Simon Bouton, Kareem Ayoub, Reena Chopra, Dominic King, Alan Karthikesalingam, Cían O. Hughes, Rosalind Raine, Julian Hughes, Dawn A. Sim, Catherine Egan, Adnan Tufail, Hugh Montgomery, Demis Hassabis, Geraint Rees, Trevor Back, Peng T. Khaw, Mustafa Suleyman, Julien Cornebise, Pearse A. Keane, and Olaf Ronneberger. 2018 · 2018
Cited alongside, same era.
Rethinking ImageNet Pre-training
Kaiming He, Ross B. Girshick, and Piotr Dollár. 2018 · 2018
Cited alongside, same era.
Do Better ImageNet Models Transfer Better?
Simon Kornblith, Jonathon Shlens, and Quoc V. Le. 2018 · 2018
Cited alongside, same era.
Shallowing Deep Networks: Layer-Wise Pruning Based on Feature Representations
S. Chen and Q. Zhao. 2019 · 2019
Cited alongside, same era.
Automatic Detection of Diabetic Retinopathy in Retinal Fundus Photographs Based on Deep Learning Algorithm
Feng Li, Zheng Liu, Hua Chen, Minshan Jiang, Xuedian Zhang, and Zhizheng Wu. 2019 · 2019
Cited alongside, same era.
CheXaid: deep learning assistance for physician diagnosis of tuberculosis using chest x-rays in patients with HIV
Pranav Rajpurkar, Chloe O’Connell, Amit Schechter, Nishit Asnani, Jason Li, Amirhossein Kiani, Robyn L Ball, Marc Mendelson, Gary Maartens, Daniël J van Hoving, et al · 2020
Later among the works it cites.
timm 0.2.1
Ross Wightman. 2020 · 2020
Later among the works it cites.
Li Zhang, Mengya Yuan, Zhen An, Xiangmei Zhao, Hui Wu, Haibin Li, Ya Wang, Beibei Sun, Huijun Li, Shibin Ding, Xiang Zeng, Ling Chao, Pan Li, and Weidong Wu. 2020 · 2020
Later among the works it cites.
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 · 2021
Closest in time.
Pranav Rajpurkar, Anirudh Joshi, Anuj Pareek, Andrew Y. Ng, and Matthew P. Lungren. 2021 · 2021
Closest in time.
Anuroop Sriram, Matthew Muckley, Koustuv Sinha, Farah Shamout, Joelle Pineau, Krzysztof J. Geras, Lea Azour, Yindalon Aphinyanaphongs, Nafissa Yakubova, and William Moore. 2021 · 2021
Closest in time.