Browsing by Author "Pirmani, Ashkan"

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    FloRank: Adaptive Structured Personalization in Federated Learning for Predicting Disability Progression in Multiple Sclerosis
    (Sage Publications Ltd, 2025) Pirmani, Ashkan; Arany, Adam; Kalincik, Tomas; Ozakbas, Serkan; Van Wijmeersch, Bart; Gouider, Riadh; Moreau, Yves
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    Citation - WoS: 3
    Citation - Scopus: 3
    Personalized Federated Learning for Predicting Disability Progression in Multiple Sclerosis Using Real-World Routine Clinical Data
    (Nature Portfolio, 2025) Pirmani, Ashkan; De Brouwer, Edward; Arany, Adam; Oldenhof, Martijn; Passemiers, Antoine; Faes, Axel; Moreau, Yves
    Early prediction of disability progression in multiple sclerosis (MS) remains challenging despite its critical importance for therapeutic decision-making. We present the first systematic evaluation of personalized federated learning (PFL) for 2-year MS disability progression prediction, leveraging multi-center real-world data from over 26,000 patients. While conventional federated learning (FL) enables privacy-aware collaborative modeling, it remains vulnerable to institutional data heterogeneity. PFL overcomes this challenge by adapting shared models to local data distributions without compromising privacy. We evaluated two personalization strategies: a novel AdaptiveDualBranchNet architecture with selective parameter sharing, and personalized fine-tuning of global models, benchmarked against centralized and client-specific approaches. Baseline FL underperformed relative to personalized methods, whereas personalization significantly improved performance, with personalized FedProx and FedAVG achieving ROC-AUC scores of 0.8398 +/- 0.0019 and 0.8384 +/- 0.0014, respectively. These findings establish personalization as critical for scalable, privacy-aware clinical prediction models and highlight its potential to inform earlier intervention strategies in MS and beyond.
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    Transforming Multiple Sclerosis Research: Advancing Disability Progression Insights Through Practical and Precise Federated Learning Using Real-World Data
    (Sage Publications Ltd, 2024) Pirmani, Ashkan; De Brouwer, Edward; Oldenhof, Martijn; Passemiers, Antoine; Van Wijmeersch, Bart; Butzkueven, Helmut; Moreau, Yves