Ce projet de recherche doctorale est publié a été réalisé par Didier DORMONT
Description d'un projet de recherche doctoral
Unsupervised learning from neuroimaging data to identify disease subtypes in Alzheimer’s disease and related disorders
Résumé du projet de recherche (Langue 1)
The objective of this PhD thesis is to develop and evaluate clinically-relevant approaches for unsupervised learning to characterize disease heterogeneity in AD and related dementias. Specific objectives include: 1) To adequately account for normal variability. For instance, in a clustering approach, the aim would be to cluster the deviations from normal variability, rather than the raw characteristics of the patients. 2) To design approaches that can handle the structure and high-dimensionality of data of neuroimaging data. 3) To define clinically-relevant measures to assess the results of the unsupervised learning.
Informations complémentaires (Langue 1)
Environment of the PhD thesis
The PhD thesis will be conducted within the ARAMIS team at the Brain and Spine Institute. ARAMIS is a joint team between CNRS, INRIA, Inserm and University Pierre et Marie Curie. The ICM is a recently created neuroscience research center within Pitié-Salpêtrière hospital in Paris. It gathers over 700 researchers covering the full spectrum of neuroscience. ARAMIS is the methodological research team of the ICM. It is a multidisciplinary research team gathering computer scientists and medical doctors. The team develops cutting-edge machine learning and image analysis approaches for multimodal medical data (neuroimaging, clinical, genetic data), in order to create new tools for diagnosis, prognosis and monitoring of brain disorders. The team has close collaborations with several clinical teams of the ICM and the Pitié Salpêtrière hospital to apply these methods to the study of neurodegenerative diseases including Alzheimer's disease, fronto-temporal dementia and Parkinson's disease. The team has a very strong network of international collaborations and in particular currently participates or coordinates two large-scale European projects funded under Horizon 2020 and two US-French grants co-funded by NIH (USA), NSF (USA) and ANR (France).
Profile of the candidate
The candidate should have a strong background in engineering (image and signal processing, machine learning, biomedical engineering). He/she should have a strong interest for multidisciplinary collaborations in the medical field. Previous experience with brain imaging data would be a plus but is not mandatory.