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Self-Supervised Learning for Anomaly Detection in Medical Neuroimaging

Scientific context

The vast majority of deep learning architectures for medical image analysis are based on supervised models requiring the collection of large datasets of annotated examples. Building such annotated datasets, which requires skilled medical experts, is time consuming and hardly achievable, especially for some specific tasks, including the detection of small and subtle lesions that are sometimes impossible to visually detect and thus manually outline. This critical aspect significantly impairs performances of supervised models and hampers their deployment in clinical neuroimaging applications, especially for brain pathologies that require the detection of small size lesions (e.g. multiple sclerosis, microbleeds) or subtle structural or morphological changes (e.g. Parkinson disease).

 

Objective and research program

To solve this challenging issue, the objective of this thesis is to develop and evaluate deep self-supervised detection and segmentation approaches whose training does not require any fine semantic annotations of the anomalies localization. We will explore different categories of self-supervised methods, including: novel unsupervised auto-encoder based anomaly detection models leveraging on the recent developments in visual transformers blocks (ViT) or vector quantized variational autoencoders (VQ-VAE), scalability of Gaussian mixture models as well as weakly supervised models based on scarce annotations.


Environment: The PhD position is granted by the “Défi IA” program sponsored by la Région Auvergne Rhône-Alpes. We offer a stimulating research environment gathering experts in Image processing, Neurosciences & Neuroimaging, Advanced Statistical and Machine Learning methods from CREATIS, Grenoble Institute of Neurosciences (GIN) and INRIA.

Location: Grenoble Neurosciences Institute: https://neurosciences.univ-grenoble-alpes.fr & CREATIS - Villeurbanne: https://www.creatis.insa-lyon.fr/. Time sharing in the two laboratories will be discussed with the selected candidates.

How to apply: Send an email directly to three supervisors with your CV and persons to contact. Interviews of the selected applicants will be done on an ongoing basis. Applications will be accepted up to the 30st of June.  


Mots-clés
analyse d'images; apprentissage automatique; deep learning; Health Data; machine learning; medical; Neuroimaging,
Établissement
Grenoble Institut Neurosciences
38000 GRENOBLE  
Directeur
Carole Lartizien, Michel Dojat
Co-encadrants
Florence Forbes
Site Web
https://www.creatis.insa-lyon.fr/site7/fr/teams; https://neurosciences.univ-grenoble-alpes.fr; https://team.inria.fr/statify/
Date de début souhaitée
03/10/2022
Langues obligatoires
Anglais; Français
Informations de contact

- GIN - team «Functional neuroimaging and brain perfusion»: Michel Dojat (michel.dojat@inserm.fr),
- CREATIS - team Myriad : Carole Lartizien (carole.lartizien@creatis.insa-lyon.fr)