Characterizing brain patterns in conversion from mild cognitive impairment (MCI) to Alzheimer's disease. - Santiago S. Silva R.; Diana L. Giraldo; Eduardo Romero 001 (SIPAIM, 2017)

Federated Learning in Distributed Medical Databases: Meta-Analysis of Large-Scale Subcortical Brain Data. - Santiago Silva 001; Boris A. Gutman; Eduardo Romero 001; Paul M. Thompson; André Altmann; Marco Lorenzi (ISBI, 2019)

Fed-BioMed: A General Open-Source Frontend Framework for Federated Learning in Healthcare. - Santiago Silva 001; André Altmann; Boris Gutman; Marco Lorenzi (DART/DCL@MICCAI, 2020)

A Probabilistic Framework for Modeling the Variability Across Federated Datasets. - Irene Balelli; Santiago Silva 001; Marco Lorenzi (IPMI, 2021)

FLamby: Datasets and Benchmarks for Cross-Silo Federated Learning in Realistic Healthcare Settings. - Jean Ogier du Terrail; Samy-Safwan Ayed; Edwige Cyffers; Felix Grimberg; Chaoyang He 001; Regis Loeb; Paul Mangold; Tanguy Marchand; Othmane Marfoq; Erum Mushtaq; Boris Muzellec; Constantin Philippenko; et al. (NeurIPS, 2022)

A Differentially Private Probabilistic Framework for Modeling the Variability Across Federated Datasets of Heterogeneous Multi-View Observations. - Irene Balelli; Santiago Silva 001; Marco Lorenzi (CoRR, 2022)

Fed-BioMed: Open, Transparent and Trusted Federated Learning for Real-world Healthcare Applications. - Francesco Cremonesi; Marc Vesin; Sergen Cansiz; Yannick Bouillard; Irene Balelli; Lucia Innocenti; Santiago Silva 001; Samy-Safwan Ayed; Riccardo Taiello; Laetitia Kameni; Richard Vidal; Fanny Orlhac; et al. (CoRR, 2023)

Federated learning of biomedical data in multicentric imaging studies. (Apprentissage fédéré de données biomédicales dans les études d'imagerie multicentriques). - Santiago Silva 001 (2023)