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DTSTART:20270328T010000
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DTSTART;TZID=Europe/Paris:20270305T113000
DTEND;TZID=Europe/Paris:20270305T113000
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SUMMARY:PhD seminar - Ninon Burgos
DESCRIPTION:Venue: Centre Broca \n\nNinon Burgos\nCNRS\nParis Brain Institute \nInvited by Natalia Khotkina (Sanpsy) \nTitle\nAI and Neuroimaging for Diagnosis in the Real World: Lessons from the AP-HP Data Warehouse \nAbstract\nThe integration of artificial intelligence and neuroimaging opens new perspectives for diagnosing neurological diseases. However\, most machine learning approaches have been validated on research datasets\, and their generalisation to clinical routine remains unclear. Hospital data warehouses offer considerable volumes of real-world data\, enabling evaluation in routine care settings. We are currently exploiting data from the Greater Paris hospitals (AP-HP\, comprising 39 hospitals and millions of patients) to develop AI tools for biomarker identification and disease prediction. This presentation will address the challenges we face related to imaging data quality and heterogeneity\, and highlight the solutions we propose. \nBiosketch\nNinon Burgos is a CNRS research director at the Paris Brain Institute\, co-head of the ARAMIS Lab\, and a fellow of PR[AI]RIE\, the PaRis Artificial Intelligence Research InstitutE. \nShe completed her PhD in 2016 at University College London\, and obtained her Habilitation (HDR) from Sorbonne Université in 2022. In 2019\, she received the ERCIM Cor Baayen Young Researcher Award.\nHer research focuses on the processing and analysis of medical images\, the use of images to guide diagnosis\, and the application of these methods to the clinic. In particular\, she has contributed to: i) anomaly detection using both traditional image processing techniques and deep generative models\, ii) the translation of machine learning approaches for quality control and computer-aided diagnosis into clinical practice\, iii) reproducible medical image processing and computer-aided diagnosis with machine learning\, and iv) the development of open-source software. \nKey publications related to the talk\n\nBottani S et al. Automatic quality control of brain T1-weighted magnetic resonance images for a clinical data warehouse. Medical Image Analysis 2022;75:102219. https://doi.org/10.1016/j.media.2021.102219.\nLoizillon S et al. Automated MRI Quality Assessment of Brain T1-weighted MRI in Clinical Data Warehouses: A Transfer Learning Approach Relying on Artefact Simulation. MELBA Journal 2024a;2:888–915. https://doi.org/10.59275/j.melba.2024-7fgd.\nBottani S et al. Evaluation of MRI-based machine learning approaches for computer-aided diagnosis of dementia in a clinical data warehouse. Medical Image Analysis 2023;89:102903. https://doi.org/10.1016/j.media.2023.102903.\nLoizillon S et al. Automatic quality control of brain 3D FLAIR MRIs for a clinical data warehouse. Medical Image Analysis 2025;103:103617. https://doi.org/10.1016/j.media.2025.103617.\nLoizillon S et al. Detecting Brain Anomalies in Clinical Routine with the $β$-VAE: Feasibility Study on Age-Related White Matter Hyperintensities. Proceedings of The 7nd International Conference on Medical Imaging with Deep Learning\, PMLR; 2024b\, p. 903–17. https://proceedings.mlr.press/v250/loizillon24a.html\n\n  \n
URL:https://www.bordeaux-neurocampus.fr/en/event/phd-seminar-mars2027/
CATEGORIES:For scientists,home-event,Monthly conferences
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