MyThisYourThat for interpretable identification of systematic bias in federated learning for biomedical images.

Autor: Naumova K; Laboratory for Intelligent Global Health and Humanitarian Response Technologies (LiGHT), Machine Learning and Optimization Laboratory, Swiss Federal Institute of Technology Lausanne (EPFL), Lausanne, Switzerland., Devos A; ETH AI Center, Swiss Federal Institute of Technology Zurich (ETH Zurich), Zurich, Switzerland., Karimireddy SP; Berkeley AI Research Laboratory, University of California, Berkeley, CA, USA.; Department of Computer Science, University of Southern California, Los Angeles, CA, USA., Jaggi M; Machine Learning and Optimization Laboratory, Swiss Federal Institute of Technology Lausanne (EPFL), Lausanne, Switzerland., Hartley MA; Laboratory for Intelligent Global Health and Humanitarian Response Technologies (LiGHT), Machine Learning and Optimization Laboratory, Swiss Federal Institute of Technology Lausanne (EPFL), Lausanne, Switzerland. mary-anne.hartley@yale.edu.; Laboratory for Intelligent Global Health and Humanitarian Response Technologies (LiGHT), School of Medicine, Yale University, New Haven, CT, USA. mary-anne.hartley@yale.edu.
Jazyk: angličtina
Zdroj: NPJ digital medicine [NPJ Digit Med] 2024 Sep 07; Vol. 7 (1), pp. 238. Date of Electronic Publication: 2024 Sep 07.
DOI: 10.1038/s41746-024-01226-1
Abstrakt: Distributed collaborative learning is a promising approach for building predictive models for privacy-sensitive biomedical images. Here, several data owners (clients) train a joint model without sharing their original data. However, concealed systematic biases can compromise model performance and fairness. This study presents MyThisYourThat (MyTH) approach, which adapts an interpretable prototypical part learning network to a distributed setting, enabling each client to visualize feature differences learned by others on their own image: comparing one client's 'This' with others' 'That'. Our setting demonstrates four clients collaboratively training two diagnostic classifiers on a benchmark X-ray dataset. Without data bias, the global model reaches 74.14% balanced accuracy for cardiomegaly and 74.08% for pleural effusion. We show that with systematic visual bias in one client, the performance of global models drops to near-random. We demonstrate how differences between local and global prototypes reveal biases and allow their visualization on each client's data without compromising privacy.
(© 2024. The Author(s).)
Databáze: MEDLINE
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