ChairesIA_2019_1 - Chaires de recherche et d'enseignement en Intelligence Artificielle - vague 1 de l'édition 2019

Transfer Learning from Big data to Small Data: Leveraging Psychiatric Neuroimaging Biomarkers Discovery – Big2small

Submission summary

Unlike many other medical specialties, psychiatry lacks objective quantitative measures (such as blood dosage) to guide clinicians in choosing a therapeutic strategy. Brain anatomy is an imprint of the individual's genetic and environmental background. The identification of prognostic brain signatures of clinical course or response to treatment would pave the way for personalized medicine in psychiatry. Many international initiatives have aggregated large datasets (>10K subjects for the general population, ~2K for patients/healthy controls cohorts). However, their large heterogeneity and cross-sectional designs prevent us from learning predictors of individual patient outcome (response to treatment, clinical evolution). Other initiatives have recently resulted in smaller, more clinically homogeneous databases (N<500) including longitudinal follow-ups to assess response to treatment and transition to disease in patients at risk. The high cost per patient (>10K€), however, limits the feasibility to scale them up to an adequate sample size (at least a few thousand) necessary to build predictive models that are sufficiently reproducible for regular clinical application.
This project proposes 3 transfer learning strategies (deep neural networks, clustering, dimensional approach of the continuum of psychiatric pathologies) to reconcile Big and small data. These 3 strategies are divided into 3 steps (i) modeling general brain variability on large bases from the general population; (ii) transfer (fine-tuning, etc.) on (medium) case-control bases to focus models on a specific pathology; (iii) final transfer on “small” longitudinal cohorts to allow learning of prognostic models of clinical evolution or response to treatments. The success of this project would demonstrate that AI could benefit health care in mental illness, which is the world‘s leading cause of disability and direct and indirect costs.

Project coordination

Edouard Duchesnay (Edouard Duchesnay)

The author of this summary is the project coordinator, who is responsible for the content of this summary. The ANR declines any responsibility as for its contents.


NEUROSPIN Edouard Duchesnay

Help of the ANR 542,726 euros
Beginning and duration of the scientific project: August 2020 - 48 Months

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