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Motivation: Traditional image attribution methods struggle to satisfactorily explain predictions of neural networks.
PadChest: A large chest x-ray image dataset with multi-label annotated reports, 1 2019
Aurelia Bustos, Antonio Pertusa, Jose-Maria Salinas, and Maria de la Iglesia-Vayá · 1901
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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, 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 · 1901
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MIMIC-CXR: A large publicly available database of labeled chest radiographs
Alistair E. W. Johnson, Tom J. Pollard, Seth J. Berkowitz, Nathaniel R. Greenbaum, Matthew P. Lungren, Chih-ying Deng, Roger G. Mark, and Steven Horng · 1901
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Saliency is a Possible Red Herring When Diagnosing Poor Generalization
Joseph D. Viviano, Becks Simpson, Francis Dutil, Yoshua Bengio, and Joseph Paul Cohen · 1910
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Explanation by Progressive Exaggeration
Sumedha Singla, Brian Pollack, Junxiang Chen, and Kayhan Batmanghelich · 1911
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On the limits of cross-domain generalization in automated X-ray prediction
Joseph Paul Cohen, Mohammad Hashir, Rupert Brooks, and Hadrien Bertrand · 2002
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Weakly Supervised Lesion Localization With Probabilistic-CAM Pooling, 2020
Wenwu Ye, Jin Yao, Hui Xue, and Yi Li · 2005
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Assessing the validity of saliency maps for abnormality localization in medical imaging
Nishanth Thumbavanam Arun, Nathan Gaw, Praveer Singh, Ken Chang, Katharina Viktoria Hoebel, Jay Patel, Mishka Gidwani, and Jayashree Kalpathy-Cramer · 2006
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Adversarial Defense by Latent Style Transformations, 2020
Shuo Wang, Surya Nepal, Marthie Grobler, Carsten Rudolph, Tianle Chen, and Shangyu Chen · 2006
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Generative Adversarial Networks
Ian J. Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio · 2014
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Conditional Generative Adversarial Nets, 2014
Mehdi Mirza and Simon Osindero · 2014
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Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps
Karen Simonyan, Andrea Vedaldi, and Andrew Zisserman · 2014
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Striving for Simplicity: The All Convolutional Net
Jost Tobias Springenberg, Alexey Dosovitskiy, Thomas Brox, and Martin Riedmiller · 2015
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Preparing a collection of radiology examinations for distribution and retrieval
Dina Demner-Fushman, Marc D. Kohli, Marc B. Rosenman, Sonya E. Shooshan, Laritza Rodriguez, Sameer Antani, George R. Thoma, and Clement J. McDonald · 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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Automatic differentiation in PyTorch
Adam Paszke, Gregory Chanan, Zeming Lin, Sam Gross, Edward Yang, Luca Antiga, and Zachary Devito · 2017
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Distribution Matching Losses Can Hallucinate Features in Medical Image Translation
Joseph Paul Cohen, Margaux Luck, and Sina Honari · 2018
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xGEMs: Generating Examplars to Explain Black-Box Models, 2018
Shalmali Joshi, Oluwasanmi Koyejo, Been Kim, and Joydeep Ghosh · 2018
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John R. Zech, Marcus A. Badgeley, Manway Liu, Anthony B. Costa, Joseph J. Titano, and Eric Karl Oermann · 2018
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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 · 2019
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Right for the Right Reasons: Training Differentiable Models by Constraining their Explanations
Andrew Ross, Michael C Hughes, and Finale Doshi-Velez · 2017
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Axiomatic Attribution for Deep Networks
Mukund Sundararajan, Ankur Taly, and Qiqi Yan · 2017
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Xiaosong Wang, Yifan Peng, Le Lu, Zhiyong Lu, Mohammadhadi Bagheri, and Ronald M. Summers · 2017
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Sanity Checks for Saliency Maps
Julius Adebayo, Justin Gilmer, Michael Muelly, Ian Goodfellow, Moritz Hardt, and Been Kim · 2018
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Visual Feature Attribution Using Wasserstein GANs, 2018
Christian F. Baumgartner, Lisa M. Koch, Kerem Can Tezcan, Jia Xi Ang, and Ender Konukoglu · 2018
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Iteratively unveiling new regions of interest in Deep Learning models
Florian Bordes, Tess Berthier, Lisa Di Jorio, Pascal Vincent, and Yoshua Bengio · 2018
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TorchXRayVision: A library of chest X-ray datasets and models
Joseph Paul Cohen, Joseph Viviano, Mohammad Hashir, and Hadrien Bertrand
Cited in the paper.
Augmenting the National Institutes of Health Chest Radiograph Dataset with Expert Annotations of Possible Pneumonia
George Shih, Carol C. Wu, Safwan S. Halabi, Marc D. Kohli, Luciano M. Prevedello, Tessa S. Cook, Arjun Sharma, Judith K. Amorosa, Veronica Arteaga, Maya Galperin-Aizenberg, Ritu R. Gill, Myrna C.B. Godoy, Stephen Hobbs, Jean Jeudy, Archana Laroia, Palmi N. Shah, Dharshan Vummidi, Kavitha Yaddanapudi, and Anouk Stein · 2019
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Assessing the (Un)Trustworthiness of Saliency Maps for Localizing Abnormalities in Medical Imaging
Nishanth Arun, Nathan Gaw, Praveer Singh, Ken Chang, Mehak Aggarwal, Bryan Chen, Katharina Hoebel, Sharut Gupta, Jay Patel, Mishka Gidwani, Julius Adebayo, Matthew D. Li, and Jayashree Kalpathy-Cramer · 2020
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Crowdsourcing pneumothorax annotations using machine learning annotations on the NIH chest X-ray dataset
Ross W. Filice, Anouk Stein, Carol C. Wu, Veronica A. Arteaga, Stephen Borstelmann, Ramya Gaddikeri, Maya Galperin-Aizenberg, Ritu R. Gill, Myrna C. Godoy, Stephen B. Hobbs, Jean Jeudy, Paras C. Lakhani, Archana Laroia, Sundeep M. Nayak, Maansi R. Parekh, Prasanth Prasanna, Palmi Shah, Dharshan Vummidi, Kavitha Yaddanapudi, and George Shih · 2020
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Captum: A unified and generic model interpretability library for PyTorch, 2020
Narine Kokhlikyan, Vivek Miglani, Miguel Martin, Edward Wang, Bilal Alsallakh, Jonathan Reynolds, Alexander Melnikov, Natalia Kliushkina, Carlos Araya, Siqi Yan, and Orion Reblitz-Richardson · 2020
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Using StyleGAN for Visual Interpretability of Deep Learning Models on Medical Images
Kathryn Schutte, Olivier Moindrot, Paul Hérent, Jean-Baptiste Schiratti, and Simon Jégou · 2020
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Explaining the Black-box Smoothly- A Counterfactual Approach, 2021
Sumedha Singla, Brian Pollack, Stephen Wallace, and Kayhan Batmanghelich · 2021
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