FeTTL: Federated Template and Task Learning for Multi-Institutional Medical Imaging
This addresses the problem of degraded model performance due to data variations in multi-institutional medical imaging, offering a solution for robust federated learning in real-world healthcare settings, though it appears incremental as it builds on existing federated learning methods.
The paper tackled domain shifts and data heterogeneity in federated learning for medical imaging by introducing FeTTL, a framework that learns a global template and task model to align data distributions, resulting in significant performance improvements over state-of-the-art baselines (p-values <0.002) on tasks like retinal fundus segmentation and metastasis classification.
Federated learning enables collaborative model training across geographically distributed medical centers while preserving data privacy. However, domain shifts and heterogeneity in data often lead to a degradation in model performance. Medical imaging applications are particularly affected by variations in acquisition protocols, scanner types, and patient populations. To address these issues, we introduce Federated Template and Task Learning (FeTTL), a novel framework designed to harmonize multi-institutional medical imaging data in federated environments. FeTTL learns a global template together with a task model to align data distributions among clients. We evaluated FeTTL on two challenging and diverse multi-institutional medical imaging tasks: retinal fundus optical disc segmentation and histopathological metastasis classification. Experimental results show that FeTTL significantly outperforms the state-of-the-art federated learning baselines (p-values <0.002) for optical disc segmentation and classification of metastases from multi-institutional data. Our experiments further highlight the importance of jointly learning the template and the task. These findings suggest that FeTTL offers a principled and extensible solution for mitigating distribution shifts in federated learning, supporting robust model deployment in real-world, multi-institutional environments.