Personalized federated learning for abdominal multi-organ segmentation based on frequency domain aggregation.

Abstract

Purpose: The training of deep learning (DL) models in medical images requires large amounts of sensitive patient data. However, acquiring adequately labeled datasets is challenging because of the heavy workload of manual annotations and the stringent privacy protocols.

Methods: Federated learning (FL) provides an alternative approach in which a coalition of clients collaboratively trains models without exchanging the underlying datasets. In this study, a novel Personalized Federated Learning Framework (PAF-Fed) is presented for abdominal multi-organ segmentation. Different from traditional FL algorithms, PAF-Fed selectively gathers partial model parameters for inter-client collaboration, retaining the remaining parameters to learn local data distributions at individual sites. Additionally, the Fourier Transform with the Self-attention mechanism is employed to aggregate the low-frequency components of parameters, promoting the extraction of shared knowledge and tackling statistical heterogeneity from diverse client datasets.

Results: The proposed method was evaluated on the Combined Healthy Abdominal Organ Segmentation magnetic resonance imaging (MRI) dataset (CHAOS 2019) and a private computed tomography (CT) dataset, achieving an average Dice Similarity Coefficient (DSC) of 72.65% for CHAOS and 85.50% for the private CT dataset, respectively.

Conclusion: The experimental results demonstrate the superiority of our PAF-Fed by outperforming state-of-the-art FL methods.