Federated Learning for Breast Density Classification: A Real-World Implementation
2020; Springer Science+Business Media; Linguagem: Inglês
10.1007/978-3-030-60548-3_18
ISSN1611-3349
AutoresHolger 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 R. F. Mendonça, Vitor Lavor, Ahmed Harouni, Colin B. Compas, Jesse Tetreault, Prerna Dogra, Yan Cheng, Selnur Erdal, Richard D. White, Behrooz Hashemian, Thomas Schultz, Miao Zhang, Adam McCarthy, Bo-Ram Yun, Elshaimaa Sharaf, Katharina Hoebel, Jay Patel, Bryan Chen, Sean Ko, Evan Leibovitz, Etta D. Pisano, Laura P. Coombs, Daguang Xu, Keith J. Dreyer, Ittai Dayan, Ram C. Naidu, Mona G. Flores, Daniel L. Rubin, Jayashree Kalpathy‐Cramer,
Tópico(s)Global Cancer Incidence and Screening
ResumoBuilding robust deep learning-based models requires large quantities of diverse training data. In this study, we investigate the use of federated learning (FL) to build medical imaging classification models in a real-world collaborative setting. Seven clinical institutions from across the world joined this FL effort to train a model for breast density classification based on Breast Imaging, Reporting & Data System (BI-RADS). We show that despite substantial differences among the datasets from all sites (mammography system, class distribution, and data set size) and without centralizing data, we can successfully train AI models in federation. The results show that models trained using FL perform 6.3% on average better than their counterparts trained on an institute's local data alone. Furthermore, we show a 45.8% relative improvement in the models' generalizability when evaluated on the other participating sites' testing data.
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