Publications of year 2025

1. Huiyu Li, Nicholas Ayache, and Hervé Delingette. Generative medical image anonymization based on latent code projection and optimization. In IEEE International Symposium on Biomedical Imaging (ISBI 2025), Houston (Texas), United States, April 2025. Keyword(s): Medical image anonymization Identityutility trade-off Latent code optimization, Medical image anonymization, Identity utility trade-off, Latent code optimization.
   

   @inproceedings{li:hal-04913904,
   TITLE = {{Generative medical image anonymization based on latent code projection and optimization}},
   AUTHOR = {Li, Huiyu and Ayache, Nicholas and Delingette, Herv{\'e}},
   url-hal= {https://inria.hal.science/hal-04913904},
   BOOKTITLE = {{IEEE International Symposium on Biomedical Imaging (ISBI 2025)}},
   ADDRESS = {Houston (Texas), United States},
   YEAR = {2025},
   MONTH = Apr,
   KEYWORDS = {Medical image anonymization Identityutility trade-off Latent code optimization ; Medical image anonymization ; Identity utility trade-off ; Latent code optimization},
   PDF = {https://inria.hal.science/hal-04913904v1/file/ISBI%20%283%29.pdf},
   HAL_ID = {hal-04913904},
   HAL_VERSION = {v1},
   
   }
   

1. Yanis Aeschlimann, Anna Calissano, Théodore Papadopoulo, and Samuel Deslauriers-Gauthier. BRAIN NETWORK ALIGNMENT USING STRUCTURAL AND FUNCTIONAL CONNECTIVITY WITH ANATOMICAL CONSTRAINTS. Note: Working paper or preprint, January 2025. Keyword(s): Structural connectivity, Functional connectivity, cortical atlas, graph alignment, inter-subject variability.
   

   @unpublished{aeschlimann:hal-04387986,
   TITLE = {{BRAIN NETWORK ALIGNMENT USING STRUCTURAL AND FUNCTIONAL CONNECTIVITY WITH ANATOMICAL CONSTRAINTS}},
   AUTHOR = {Aeschlimann, Yanis and Calissano, Anna and Papadopoulo, Th{\'e}odore and Deslauriers-Gauthier, Samuel},
   url-hal= {https://hal.science/hal-04387986},
   NOTE = {working paper or preprint},
   YEAR = {2025},
   MONTH = Jan,
   KEYWORDS = {Structural connectivity ; Functional connectivity ; cortical atlas ; graph alignment ; inter-subject variability},
   PDF = {https://hal.science/hal-04387986v2/file/ISBI_2025_Yanis_reviewed_version.pdf},
   HAL_ID = {hal-04387986},
   HAL_VERSION = {v2},
   
   }
   




2. Francesco Cremonesi, Lucie Chambon, Nelson Mokkadem, Huyen Thi Trang Nguyen, Oliver Humbert, and Marco Lorenzi. Knowledge-based semantic enrichment of medical imaging data for automatic phenotyping and pattern discovery in metastatic lung cancer. Note: Working paper or preprint, February 2025.
   

   @unpublished{cremonesi:hal-04879690,
   TITLE = {{Knowledge-based semantic enrichment of medical imaging data for automatic phenotyping and pattern discovery in metastatic lung cancer}},
   AUTHOR = {Cremonesi, Francesco and Chambon, Lucie and Mokkadem, Nelson and Nguyen, Huyen Thi Trang and Humbert, Oliver and Lorenzi, Marco},
   url-hal= {https://hal.science/hal-04879690},
   NOTE = {working paper or preprint},
   YEAR = {2025},
   MONTH = Feb,
   PDF = {https://hal.science/hal-04879690v1/file/whitepaper.pdf},
   HAL_ID = {hal-04879690},
   HAL_VERSION = {v1},
   
   }
   




3. 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.
   

   @unpublished{cremonesi:hal-04893012,
   TITLE = {{A cautionary tale on the cost-effectiveness of collaborative AI in real-world medical applications}},
   AUTHOR = {Cremonesi, Francesco and Innocenti, Lucia and Ourselin, Sebastien and Goh, Vicky and Antonelli, Michela and Lorenzi, Marco},
   url-hal= {https://hal.science/hal-04893012},
   NOTE = {working paper or preprint},
   YEAR = {2025},
   MONTH = Jan,
   KEYWORDS = {Collaborative learning ; healthcare ; sustainable AI ; trustworthy AI ; federated learning consensus-based learning ; medical imaging},
   PDF = {https://hal.science/hal-04893012v1/file/2412.06494v1.pdf},
   HAL_ID = {hal-04893012},
   HAL_VERSION = {v1},
   
   }
   

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