11 citations · 12 across the 4 of their papers we have counts for
4 papers
A cautionary tale on the cost-effectiveness of collaborative AI in real-world medical applications
Francesco Cremonesi, Lucia Innocenti, Sebastien Ourselin +3
Federated learning (FL) has gained wide popularity as a collaborative learning paradigm allowing collaborative Artificial Intelligence (AI) in sensitive healthcare applications. Ne…
Enhancing Privacy in Federated Learning: Secure Aggregation for Real-World Healthcare Applications
Riccardo Taiello, Sergen Cansiz, Marc Vesin +4
Deploying federated learning (FL) in real-world scenarios, particularly in healthcare, poses challenges in communication and security. In particular, with respect to the federated…
Benchmarking Collaborative Learning Methods Cost-Effectiveness for Prostate Segmentation
Lucia Innocenti, Michela Antonelli, Francesco Cremonesi +5
Healthcare data is often split into medium/small-sized collections across multiple hospitals and access to it is encumbered by privacy regulations. This brings difficulties to use…
Fed-BioMed: Open, Transparent and Trusted Federated Learning for Real-world Healthcare Applications
Francesco Cremonesi, Marc Vesin, Sergen Cansiz +16
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, t…