Work

FUSION: Uncertainty‐Guided Federated Semi‐Supervised Learning for Medical Image Segmentation

Year: 2025

Type: article

Abstract: ABSTRACT Federated learning (FL) for medical image segmentation poses critical challenges, including non‐IID data distributions, limited access to labelled annotations, and stringent privacy constraints across institutions. To address these, we propose FUSION (Federated Unified Semi‐Supervised Optimisation Network), a novel dual‐path training framework that integrates both Federated Labelled Data Learning (FLDL) and Federated Unlabelled Data Training (FUDT). Central to FUSION is a two‐stage pseudo‐label refinement strategy designed to ensure robustness under real‐world federated constraints. First, synthetic label denoising is performed using Monte Carlo dropout‐based uncertainty estimation, enabling clients to identify and exclude low‐confidence predictions. Second, prototype‐based correction is applied to further refine pseudo‐labels by aligning them with class‐specific feature centroids, mitigating errors caused by domain shifts and inter‐client variability. These refined labels are used for localised training on unlabelled clients, while a dynamic aggregation scheme modulated by a reliability‐based hyperparameter μ adjusts the influence of labelled versus unlabelled clients during global model updates. This tightly coupled interaction between pseudo‐label quality and federated optimisation ensures stability, accelerates convergence, and enhances generalisation across heterogeneous clients. FUSION is evaluated on three diverse datasets: TCGA‐LGG (brain MRI), Kvasir‐SEG (colonoscopy), and UDIAT (ultrasound) and consistently outperforms state‐of‐the‐art FL models in Dice, IoU, HD95, and ASD metrics. Results confirm the critical role of synthetic label refinement in enhancing segmentation accuracy, boundary precision, and model scalability. FUSION provides a technically grounded, privacy‐preserving, and label‐efficient solution for real‐world multi‐institutional medical image segmentation tasks.

Source: IET Image Processing

DOI: 10.1049/ipr2.70147

Authors: Abdul Raheem, Zhen Yang, Haiyang Yu, Malik Abdul Manan, Fahad Sabah, Shahzad Ahmed

Institution: Beijing University of Technology

Language: English

Topic: Privacy-Preserving Technologies in Data

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $2,740

Funders: National Natural Science Foundation of China, Natural Science Foundation of Beijing Municipality, National Key Research and Development Program of China