Federated Learning for Breast Density Classification: A Real-World Implementation-Reference-Cited by-同舟云学术

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

Published:2020 Issue: Volume: Page:181-191
ISSN:0302-9743
Container-title:Domain Adaptation and Representation Transfer, and Distributed and Collaborative Learning
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Author:

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

Publisher

Springer International Publishing

Link

https://link.springer.com/content/pdf/10.1007/978-3-030-60548-3_18

Reference26 articles.

1. Baweja, C., Glocker, B., Kamnitsas, K.: Towards continual learning in medical imaging. arXiv preprint arXiv:1811.02496 (2018)

2. Boyd, N., et al.: Quantitative classification of mammographic densities and breast cancer risk: results from the Canadian national breast screening study. JNCI J. Nat. Cancer Inst. 87(9), 670–675 (1995)

3. Boyd, N.F., et al.: Mammographic density and the risk and detection of breast cancer. N. Engl. J. Med. 356(3), 227–236 (2007)

4. Chang, K., et al.: Distributed deep learning networks among institutions for medical imaging. J. Am. Med. Inform. Assoc. 25(8), 945–954 (2018)

5. Chang, K., et al.: Multi-institutional assessment and crowdsourcing evaluation of deep learning for automated classification of breast density. J. Am. Coll. Radiol. (2020). https://doi.org/10.1016/j.jacr.2020.05.015

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