Fair evaluation of federated learning algorithms for automated breast density classification: The results of the 2022 ACR-NCI-NVIDIA federated learning challenge.

Autor: Schmidt K; American College of Radiology, USA. Electronic address: kschmidt@acr.org., Bearce B; The Massachusetts General Hospital, USA; University of Colorado, USA., Chang K; The Massachusetts General Hospital, USA., Coombs L; American College of Radiology, USA., Farahani K; National Institutes of Health National Cancer Institute, USA., Elbatel M; Computer Vision and Robotics Institute, University of Girona, Spain., Mouheb K; Computer Vision and Robotics Institute, University of Girona, Spain., Marti R; Computer Vision and Robotics Institute, University of Girona, Spain., Zhang R; Cooperative Medianet Innovation Center, Shanghai Jiao Tong University, China; Shanghai AI Laboratory, China., Zhang Y; Shanghai AI Laboratory, China., Wang Y; Cooperative Medianet Innovation Center, Shanghai Jiao Tong University, China; Shanghai AI Laboratory, China., Hu Y; Real Doctor AI Research Centre, Zhejiang University, China., Ying H; Real Doctor AI Research Centre, Zhejiang University, China; School of Public Health, Zhejiang University, China., Xu Y; Real Doctor AI Research Centre, Zhejiang University, China; College of Computer Science and Technology, Zhejiang University, China., Testagrose C; University of North Florida College of Computing Jacksonville, USA., Demirer M; Mayo Clinic Florida Radiology, USA., Gupta V; Mayo Clinic Florida Radiology, USA., Akünal Ü; Division of Medical Image Computing, German Cancer Research Center, Heidelberg, Germany., Bujotzek M; Division of Medical Image Computing, German Cancer Research Center, Heidelberg, Germany., Maier-Hein KH; Division of Medical Image Computing, German Cancer Research Center, Heidelberg, Germany., Qin Y; Electronic and Computer Engineering, Hong Kong University of Science and Technology, China., Li X; Electronic and Computer Engineering, Hong Kong University of Science and Technology, China., Kalpathy-Cramer J; The Massachusetts General Hospital, USA; University of Colorado, USA., Roth HR; NVIDIA, USA.
Jazyk: angličtina
Zdroj: Medical image analysis [Med Image Anal] 2024 Jul; Vol. 95, pp. 103206. Date of Electronic Publication: 2024 May 15.
DOI: 10.1016/j.media.2024.103206
Abstrakt: The correct interpretation of breast density is important in the assessment of breast cancer risk. AI has been shown capable of accurately predicting breast density, however, due to the differences in imaging characteristics across mammography systems, models built using data from one system do not generalize well to other systems. Though federated learning (FL) has emerged as a way to improve the generalizability of AI without the need to share data, the best way to preserve features from all training data during FL is an active area of research. To explore FL methodology, the breast density classification FL challenge was hosted in partnership with the American College of Radiology, Harvard Medical Schools' Mass General Brigham, University of Colorado, NVIDIA, and the National Institutes of Health National Cancer Institute. Challenge participants were able to submit docker containers capable of implementing FL on three simulated medical facilities, each containing a unique large mammography dataset. The breast density FL challenge ran from June 15 to September 5, 2022, attracting seven finalists from around the world. The winning FL submission reached a linear kappa score of 0.653 on the challenge test data and 0.413 on an external testing dataset, scoring comparably to a model trained on the same data in a central location.
Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
(Copyright © 2024 Elsevier B.V. All rights reserved.)
Databáze: MEDLINE
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