Self-supervised learning for the detection of brain anomalies in MRI imaging

15/07/2022 – 16/07/2022 all-day

Offre en lien avec l’Action/le Réseau : – — –/– — –

Laboratoire/Entreprise : Collaboration GIN/CREATIS/INRIA
Durée : 36 mois
Contact :
Date limite de publication : 2022-07-15

Contexte :
Key words: Machine learning, Deep Learning; Multidimensional data, Segmentation, Neuroimaging, Self-supervised learning, Anomaly detection, Unsupervised representation learning

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).

Sujet :
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.
(see attached file for details)

Profil du candidat :
We are looking for an enthusiastic and autonomous student with strong motivation and interest in multidisciplinary research (image processing and machine learning in a medical context).

Formation et compétences requises :
Candidate should have strong background either in machine learning and/or deep learning or image processing and some experience in both fields as well as good programming skills.

Adresse d’emploi :
Location: Grenoble Neurosciences Institute: & CREATIS – Villeurbanne: Time sharing in the two laboratories will be discussed with the selected candidates.

Document attaché : 202203281242_PhD_proposal_Self_Supervised_Learning_Neuroimaging_CREATIS_GIN_INRIA.pdf