Publications of Lucia Innocenti

1. Riccardo Taiello, Sergen Cansiz, Marc Vesin, Francesco Cremonesi, Lucia Innocenti, Melek Önen, and Marco Lorenzi. Enhancing Privacy in Federated Learning: Secure Aggregation for Real-World Healthcare Applications. In Springer, editor, Lecture notes in computer science, volume LNCS-15274 of Medical Image Computing and Computer Assisted Intervention -- MICCAI 2024 Workshops, Marrachech, Morocco, pages 204-214, October 2024. Keyword(s): Federated Learning, Secure Aggregation, Healthcare Applications. [bibtex-entry]



2. Yann Fraboni, Lucia Innocenti, Michela Antonelli, Richard Vidal, Laetitia Kameni, Sebastien Ourselin, and Marco Lorenzi. Validation of Federated Unlearning on Collaborative Prostate Segmentation. In Lecture Notes in Computer Science, volume 14393 of Lecture Notes in Computer Science, Toronto, Canada, pages 322-333, October 2023. Medical Image Computing and Computer Assisted Intervention, Springer Nature Switzerland. Keyword(s): federated unlearning, prostate cancer, segmentation, Medical imaging. [bibtex-entry]



3. Lucia Innocenti, Michela Antonelli, Francesco Cremonesi, Kenaan Sarhan, Alejandro Granados, Vicky Goh, Sebastien Ourselin, and Marco Lorenzi. Benchmarking Collaborative Learning Methods Cost-Effectiveness for Prostate Segmentation. In ECML - PharML - Applications of Machine Learning in Pharma and Healthcare (Workshop at ECML PKDD 2023), Turin (IT), Italy, September 2023. arXiv. Note: Workshop at ECML PKDD 2023. Keyword(s): Collaborative Learning, Cost-Effectiveness, Prostate Segmentation. [bibtex-entry]

1. Francesco Cremonesi, Lucia Innocenti, Sebastien Ourselin, Vicky Goh, Michela Antonelli, and Marco Lorenzi. A cautionary tale on the cost-effectiveness of collaborative AI in real-world medical applications. Note: Working paper or preprint, January 2025. Keyword(s): Collaborative learning, healthcare, sustainable AI, trustworthy AI, federated learning consensus-based learning, medical imaging. [bibtex-entry]



2. 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, and Marco Lorenzi. Fed-BioMed: Open, Transparent and Trusted Federated Learning for Real-world Healthcare Applications. Note: Working paper or preprint, April 2023. Keyword(s): Machine learning, Biomedical Application, Healthcare, Federated Learning Framework. [bibtex-entry]

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