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Self supervised learning for anomaly detection in medical neuroimaging

28 Mars 2022


Catégorie : Doctorant


This PhD proposal is part of a granted research project between Grenoble Institute of Neuroscience (GIN), Centre de Recherche en Acquisition et Traitement de l'Image pour la Santé (CREATIS) and INRIA Grenoble. Please get more information on this PhD proposal here : https://www.creatis.insa-lyon.fr/site7/fr/node/47195

 

This PhD proposal is part of a granted research project between Grenoble Institute of Neuroscience (GIN), Centre de Recherche en Acquisition et Traitement de l'Image pour la Santé (CREATIS) and INRIA Grenoble.

Please get more information on this PhD proposal here :

https://www.creatis.insa-lyon.fr/site7/fr/node/47195

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. During the PhD thesis, new methodological research axes will be considered based on the prolific literature in this field. 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.

In a first step, we will focus on Parkinson disease and micro hemorrhage imaging data and fuse different MR modalities.

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

 

How to apply: Send an email directly to the 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.