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Jayashree Kalpathy-Cramer

Not Color Blind: AI Predicts Racial Identity from Black and White Retinal Vessel Segmentations


Sep 28, 2021
Aaron S. Coyner, Praveer Singh, James M. Brown, Susan Ostmo, R. V. Paul Chan, Michael F. Chiang, Jayashree Kalpathy-Cramer, J. Peter Campbell

* 31 pages, 6 figures 

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Deploying clinical machine learning? Consider the following...


Sep 14, 2021
Charles Lu, Ken Chang, Praveer Singh, Stuart Pomerantz, Sean Doyle, Sujay Kakarmath, Christopher Bridge, Jayashree Kalpathy-Cramer


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Fair Conformal Predictors for Applications in Medical Imaging


Sep 09, 2021
Charles Lu, Andreanne Lemay, Ken Chang, Katharina Hoebel, Jayashree Kalpathy-Cramer


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Evaluating subgroup disparity using epistemic uncertainty in mammography


Jul 15, 2021
Charles Lu, Andreanne Lemay, Katharina Hoebel, Jayashree Kalpathy-Cramer

* Accepted to the Interpretable Machine Learning in Healthcare workshop at the ICML 2021 conference 

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SplitAVG: A heterogeneity-aware federated deep learning method for medical imaging


Jul 06, 2021
Miao Zhang, Liangqiong Qu, Praveer Singh, Jayashree Kalpathy-Cramer, Daniel L. Rubin


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The RSNA-ASNR-MICCAI BraTS 2021 Benchmark on Brain Tumor Segmentation and Radiogenomic Classification


Jul 05, 2021
Ujjwal Baid, Satyam Ghodasara, Michel Bilello, Suyash Mohan, Evan Calabrese, Errol Colak, Keyvan Farahani, Jayashree Kalpathy-Cramer, Felipe C. Kitamura, Sarthak Pati, Luciano M. Prevedello, Jeffrey D. Rudie, Chiharu Sako, Russell T. Shinohara, Timothy Bergquist, Rong Chai, James Eddy, Julia Elliott, Walter Reade, Thomas Schaffter, Thomas Yu, Jiaxin Zheng, BraTS Annotators, Christos Davatzikos, John Mongan, Christopher Hess, Soonmee Cha, Javier Villanueva-Meyer, John B. Freymann, Justin S. Kirby, Benedikt Wiestler, Priscila Crivellaro, Rivka R. Colen, Aikaterini Kotrotsou, Daniel Marcus, Mikhail Milchenko, Arash Nazeri, Hassan Fathallah-Shaykh, Roland Wiest, Andras Jakab, Marc-Andre Weber, Abhishek Mahajan, Bjoern Menze, Adam E. Flanders, Spyridon Bakas

* 17 pages, 2 figures, 1 table 

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Highdicom: A Python library for standardized encoding of image annotations and machine learning model outputs in pathology and radiology


Jun 14, 2021
Christopher P. Bridge, Chris Gorman, Steven Pieper, Sean W. Doyle, Jochen K. Lennerz, Jayashree Kalpathy-Cramer, David A. Clunie, Andriy Y. Fedorov, Markus D. Herrmann


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Addressing catastrophic forgetting for medical domain expansion


Mar 24, 2021
Sharut Gupta, Praveer Singh, Ken Chang, Liangqiong Qu, Mehak Aggarwal, Nishanth Arun, Ashwin Vaswani, Shruti Raghavan, Vibha Agarwal, Mishka Gidwani, Katharina Hoebel, Jay Patel, Charles Lu, Christopher P. Bridge, Daniel L. Rubin, Jayashree Kalpathy-Cramer

* First three authors contributed equally 

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The unreasonable effectiveness of Batch-Norm statistics in addressing catastrophic forgetting across medical institutions


Nov 16, 2020
Sharut Gupta, Praveer Singh, Ken Chang, Mehak Aggarwal, Nishanth Arun, Liangqiong Qu, Katharina Hoebel, Jay Patel, Mishka Gidwani, Ashwin Vaswani, Daniel L Rubin, Jayashree Kalpathy-Cramer

* Accepted as oral presentation in Machine Learning for Health (ML4H) at NeurIPS 2020 - Extended Abstract ; 6 pages and 4 figures 

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Towards Trainable Saliency Maps in Medical Imaging


Nov 15, 2020
Mehak Aggarwal, Nishanth Arun, Sharut Gupta, Ashwin Vaswani, Bryan Chen, Matthew Li, Ken Chang, Jay Patel, Katherine Hoebel, Mishka Gidwani, Jayashree Kalpathy-Cramer, Praveer Singh

* Machine Learning for Health (ML4H) at NeurIPS 2020 - Extended Abstract 

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Federated Learning for Breast Density Classification: A Real-World Implementation


Sep 17, 2020
Holger R. Roth, Ken Chang, Praveer Singh, Nir Neumark, Wenqi Li, Vikash Gupta, Sharut Gupta, Liangqiong Qu, Alvin Ihsani, Bernardo C. Bizzo, Yuhong Wen, Varun Buch, Meesam Shah, Felipe Kitamura, Matheus Mendonça, Vitor Lavor, Ahmed Harouni, Colin Compas, Jesse Tetreault, Prerna Dogra, Yan Cheng, Selnur Erdal, Richard White, Behrooz Hashemian, Thomas Schultz, Miao Zhang, Adam McCarthy, B. Min Yun, Elshaimaa Sharaf, Katharina V. Hoebel, Jay B. Patel, Bryan Chen, Sean Ko, Evan Leibovitz, Etta D. Pisano, Laura Coombs, Daguang Xu, Keith J. Dreyer, Ittai Dayan, Ram C. Naidu, Mona Flores, Daniel Rubin, Jayashree Kalpathy-Cramer

* Accepted at the 1st MICCAI Workshop on "Distributed And Collaborative Learning"; add citation to Fig. 1 & 2 and update Fig. 5 

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Assessing the (Un)Trustworthiness of Saliency Maps for Localizing Abnormalities in Medical Imaging


Aug 06, 2020
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, Jayashree Kalpathy-Cramer

* Submitted to Nature Machine Intelligence. First four authors contributed equally to this work 

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Assessing the validity of saliency maps for abnormality localization in medical imaging


May 29, 2020
Nishanth Thumbavanam Arun, Nathan Gaw, Praveer Singh, Ken Chang, Katharina Viktoria Hoebel, Jay Patel, Mishka Gidwani, Jayashree Kalpathy-Cramer


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Split Learning for collaborative deep learning in healthcare


Dec 27, 2019
Maarten G. Poirot, Praneeth Vepakomma, Ken Chang, Jayashree Kalpathy-Cramer, Rajiv Gupta, Ramesh Raskar

* Workshop paper: 8 pages, 2 figures, 1 table 

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Give me (un)certainty -- An exploration of parameters that affect segmentation uncertainty


Nov 14, 2019
Katharina Hoebel, Ken Chang, Jay Patel, Praveer Singh, Jayashree Kalpathy-Cramer

* Machine Learning for Health (ML4H) at NeurIPS 2019 - Extended Abstract 

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ExpertMatcher: Automating ML Model Selection for Clients using Hidden Representations


Oct 09, 2019
Vivek Sharma, Praneeth Vepakomma, Tristan Swedish, Ken Chang, Jayashree Kalpathy-Cramer, Ramesh Raskar

* In NeurIPS Workshop on Robust AI in Financial Services: Data, Fairness, Explainability, Trustworthiness, and Privacy, 2019 

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ExpertMatcher: Automating ML Model Selection for Users in Resource Constrained Countries


Oct 05, 2019
Vivek Sharma, Praneeth Vepakomma, Tristan Swedish, Ken Chang, Jayashree Kalpathy-Cramer, Ramesh Raskar

* In NeurIPS Workshop on Machine learning for the Developing World (ML4D) 

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Accelerated Experimental Design for Pairwise Comparisons


Jan 18, 2019
Yuan Guo, Jennifer Dy, Deniz Erdogmus, Jayashree Kalpathy-Cramer, Susan Ostmo, J. Peter Campbell, Michael F. Chiang, Stratis Ioannidis


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Semi-Supervised Deep Learning for Abnormality Classification in Retinal Images


Dec 19, 2018
Bruno Lecouat, Ken Chang, Chuan-Sheng Foo, Balagopal Unnikrishnan, James M. Brown, Houssam Zenati, Andrew Beers, Vijay Chandrasekhar, Jayashree Kalpathy-Cramer, Pavitra Krishnaswamy

* Machine Learning for Health (ML4H) Workshop at NeurIPS 2018 arXiv:1811.07216 

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Deep feature transfer between localization and segmentation tasks


Nov 10, 2018
Szu-Yeu Hu, Andrew Beers, Ken Chang, Kathi Höbel, J. Peter Campbell, Deniz Erdogumus, Stratis Ioannidis, Jennifer Dy, Michael F. Chiang, Jayashree Kalpathy-Cramer, James M. Brown


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DeepNeuro: an open-source deep learning toolbox for neuroimaging


Aug 14, 2018
Andrew Beers, James Brown, Ken Chang, Katharina Hoebel, Elizabeth Gerstner, Bruce Rosen, Jayashree Kalpathy-Cramer


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High-resolution medical image synthesis using progressively grown generative adversarial networks


May 09, 2018
Andrew Beers, James Brown, Ken Chang, J. Peter Campbell, Susan Ostmo, Michael F. Chiang, Jayashree Kalpathy-Cramer


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Institutionally Distributed Deep Learning Networks


Sep 10, 2017
Ken Chang, Niranjan Balachandar, Carson K Lam, Darvin Yi, James M Brown, Andrew Beers, Bruce R Rosen, Daniel L Rubin, Jayashree Kalpathy-Cramer


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Sequential 3D U-Nets for Biologically-Informed Brain Tumor Segmentation


Sep 09, 2017
Andrew Beers, Ken Chang, James Brown, Emmett Sartor, CP Mammen, Elizabeth Gerstner, Bruce Rosen, Jayashree Kalpathy-Cramer


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Accelerated Reconstruction of Perfusion-Weighted MRI Enforcing Jointly Local and Nonlocal Spatio-temporal Constraints


Aug 25, 2017
Cagdas Ulas, Christine Preibisch, Jonathan Sperl, Thomas Pyka, Jayashree Kalpathy-Cramer, Bjoern Menze

* Submission to IEEE Transactions on Medical Imaging (August 2017) 

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