Benchmarking Collaborative Learning Methods Cost-Effectiveness for Prostate Segmentation

no code implementations • 29 Sep 2023 • Lucia Innocenti, Michela Antonelli, Francesco Cremonesi, Kenaan Sarhan, Alejandro Granados, Vicky Goh, Sebastien Ourselin, Marco Lorenzi

To the best of our knowledge, this is the first work in which CBM, such as label fusion techniques, are used to solve a problem of collaborative learning.

Benchmarking Federated Learning +1

Fed-BioMed: Open, Transparent and Trusted Federated Learning for Real-world Healthcare Applications

no code implementations • 24 Apr 2023 • Francesco Cremonesi, Marc Vesin, Sergen Cansiz, Yannick Bouillard, Irene Balelli, Lucia Innocenti, Santiago Silva, Samy-Safwan Ayed, Riccardo Taiello, Laetita Kameni, Richard Vidal, Fanny Orlhac, Christophe Nioche, Nathan Lapel, Bastien Houis, Romain Modzelewski, Olivier Humbert, Melek Önen, Marco Lorenzi

The real-world implementation of federated learning is complex and requires research and development actions at the crossroad between different domains ranging from data science, to software programming, networking, and security.

Federated Learning

Fed-MIWAE: Federated Imputation of Incomplete Data via Deep Generative Models

no code implementations • 17 Apr 2023 • Irene Balelli, Aude Sportisse, Francesco Cremonesi, Pierre-Alexandre Mattei, Marco Lorenzi

In addition, thanks to the variational nature of Fed-MIWAE, our method is designed to perform multiple imputation, allowing for the quantification of the imputation uncertainty in the federated scenario.

Federated Learning Imputation