Adam McCarthy
Harvard University
3 Papers
31 Citations
Adam McCarthy is an academic researcher from Harvard University. The author has contributed to research in topics: Deep learning & Computer science. The author has an hindex of 3, co-authored 3 publications.
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Papers
Federated Learning for Breast Density Classification: A Real-World Implementation.
Holger R. Roth,Ken Chang,Praveer Singh,Nir Neumark,Wenqi Li,Vikash Gupta,Sharut Gupta,Liangqiong Qu,Alvin Ihsani,Bernardo Bizzo,Yuhong Wen,Varun Buch,Meesam Shah,Felipe Kitamura,Matheus Ribeiro Furtado de Mendonça,Vitor Lavor,Ahmed Harouni,Colin B. Compas,Jesse Tetreault,Prerna Dogra,Yan Cheng,Selnur Erdal,Richard D. White,Behrooz Hashemian,Thomas J. Schultz,Miao Zhang,Adam McCarthy,B. Min Yun,Elshaimaa Sharaf,Katharina 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 L. Rubin,Jayashree Kalpathy-Cramer +42 more
- 08 Oct 2020
TL;DR: This study investigates the use of federated learning (FL) to build medical imaging classification models in a real-world collaborative setting and shows that despite substantial differences among the datasets from all sites and without centralizing data, it can successfully train AI models in federation.
Federated Learning for Breast Density Classification: A Real-World Implementation
Holger R. Roth,Ken Chang,Praveer Singh,Nir Neumark,Wenqi Li,Vikash Gupta,Sharut Gupta,Liangqiong Qu,Alvin Ihsani,Bernardo Bizzo,Yuhong Wen,Varun Buch,Meesam Shah,Felipe Kitamura,Matheus Ribeiro Furtado de Mendonça,Vitor Lavor,Ahmed Harouni,Colin B. Compas,Jesse Tetreault,Prerna Dogra,Yan Cheng,Selnur Erdal,Richard D. White,Behrooz Hashemian,Thomas J. Schultz,Miao Zhang,Adam McCarthy,B. Min Yun,Elshaimaa Sharaf,Katharina 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 L. Rubin,Jayashree Kalpathy-Cramer +42 more
TL;DR: This paper investigated the use of federated learning (FL) to build medical imaging classification models in a real-world collaborative setting, and showed that models trained using FL perform 6.3% on average better than their counterparts trained on an institute's local data alone.
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Detection and Delineation of Acute Cerebral Infarct on DWI Using Weakly Supervised Machine Learning
Stefano Pedemonte,Bernardo Bizzo,Stuart R. Pomerantz,Neil A. Tenenholtz,Bradley Wright,Mark Walters,Sean Doyle,Adam McCarthy,Renata R. Almeida,Katherine P. Andriole,Mark Michalski,R. Gilberto Gonzalez +11 more
- 16 Sep 2018
TL;DR: YNet is presented as a novel fully-automated deep learning algorithm for detection and volumetric segmentation and quantification of acute cerebral ischemic lesions from DWI that enables the combination of both weak labels derived from radiology report classification and manually delineated pixel level training data.
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