ArticleJournal of applied clinical medical physics2025
Personalized federated learning for abdominal multi-organ segmentation based on frequency domain aggregation.
Article in Journal of applied clinical medical physics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Liver Semantic Segmentation Method Based on Multi-Channel Feature Extraction and Cross Fusion.Bioengineering (Basel, Switzerland) · 2025Article
- Personalized federated learning for abdominal multi-organ segmentation based on frequency domain aggregation.Journal of applied clinical medical physics · 2025Article
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Authors and funding
4 authors.
Funding
Abstract
purposeThe 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.
methodsFederated 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.
resultsThe 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.
conclusionThe experimental results demonstrate the superiority of our PAF-Fed by outperforming state-of-the-art FL methods.
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