Abstract
Federated learning (FL), training deep models from decentralized data without privacy leakage, has shown great potential in medical image computing recently. However, considering the ubiquitous class imbalance in medical data, FL can exhibit performance degradation, especially for minority classes (e.g. rare diseases). Existing methods towards this problem mainly focus on training a balanced classifier to eliminate class prior bias among classes, but neglect to explore better representation to facilitate classification performance. In this paper, we present a privacy-preserving FL method named FedIIC to combat class imbalance from two perspectives: feature learning and classifier learning. In feature learning, two levels of contrastive learning are designed to extract better class-specific features with imbalanced data in FL. In classifier learning, per-class margins are dynamically set according to real-time difficulty and class priors, which helps the model learn classes equally. Experimental results on publicly-available datasets demonstrate the superior performance of FedIIC in dealing with both real-world and simulated multi-source medical imaging data under class imbalance. Code is available at https://github.com/wnn2000/FedIIC.
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Acknowledgement
This work was supported in part by the National Natural Science Foundation of China under Grants 62202179 and 62271220, in part by the Natural Science Foundation of Hubei Province of China under Grant 2022CFB585, and in part by the Research Grants Council GRF Grant 16203319. The computation is supported by the HPC Platform of HUST.
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Wu, N., Yu, L., Yang, X., Cheng, KT., Yan, Z. (2023). FedIIC: Towards Robust Federated Learning for Class-Imbalanced Medical Image Classification. In: Greenspan, H., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2023. MICCAI 2023. Lecture Notes in Computer Science, vol 14221. Springer, Cham. https://doi.org/10.1007/978-3-031-43895-0_65
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