Semi-automatic three-dimensional vessel segmentation using a connected component localization of the Region-Scalable Fitting Energy. - Marco Fedele; Elena Faggiano; Luca Barbarotta; Francesco Cremonesi; Luca Formaggia; Simona Perotto (ISPA, 2015)

Simulating Morphologically Detailed Neuronal Networks at Extreme Scale. - Aleksandr Ovcharenko; Pramod S. Kumbhar; Michael L. Hines; Francesco Cremonesi; Timothée Ewart; Stuart Yates; Felix Schürmann; Fabien Delalondre (PARCO, 2015)

Performance evaluation of the IBM POWER8 architecture to support computational neuroscientific application using morphologically detailed neurons. - Timothée Ewart; Stuart Yates; Francesco Cremonesi; Pramod S. Kumbhar; Felix Schürmann; Fabien Delalondre (PMBS@SC, 2015)

Neuromapp: A Mini-application Framework to Improve Neural Simulators. - Timothée Ewart; Judit Planas; Francesco Cremonesi; Kai Langen; Felix Schürmann; Fabien Delalondre (ISC, 2017)

Analytic performance modeling and analysis of detailed neuron simulations. - Francesco Cremonesi; Georg Hager; Gerhard Wellein; Felix Schürmann (Int. J. High Perform. Comput. Appl., 2020)

Understanding Computational Costs of Cellular-Level Brain Tissue Simulations Through Analytical Performance Models. - Francesco Cremonesi; Felix Schürmann (Neuroinformatics, 2020)

Polynomial Evaluation on Superscalar Architecture, Applied to the Elementary Function ex. - Timothée Ewart; Francesco Cremonesi; Felix Schürmann; Fabien Delalondre (ACM Trans. Math. Softw., 2020)

Benchmarking Collaborative Learning Methods Cost-Effectiveness for Prostate Segmentation. - Lucia Innocenti; Michela Antonelli; Francesco Cremonesi; Kenaan Sarhan; Alejandro Granados; Vicky Goh; Sébastien Ourselin; Marco Lorenzi (PKDD/ECML Workshops (4), 2023)

Fed-MIWAE: Federated Imputation of Incomplete Data via Deep Generative Models. - Irene Balelli; Aude Sportisse; Francesco Cremonesi; Pierre-Alexandre Mattei; Marco Lorenzi (CoRR, 2023)

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)

The need for multimodal health data modeling: A practical approach for a federated-learning healthcare platform. - Francesco Cremonesi; Vincent Planat; Varvara Kalokyri; Haridimos Kondylakis; Tiziana Sanavia; Victor Miguel Mateos Resinas; Babita Singh; Silvia Uribe (J. Biomed. Informatics, 2023)

Enhancing Privacy in Federated Learning: Secure Aggregation for Real-World Healthcare Applications. - Riccardo Taiello; Sergen Cansiz; Marc Vesin; Francesco Cremonesi; Lucia Innocenti; Melek Önen; Marco Lorenzi (ISIC/iMIMIC/EARTH/DeCaF@MICCAI, 2024)

A cautionary tale on the cost-effectiveness of collaborative AI in real-world medical applications. - Francesco Cremonesi; Lucia Innocenti; Sébastien Ourselin; Vicky Goh; Michela Antonelli; Marco Lorenzi (CoRR, 2024)