PhD position in Self-Supervised Learning for Anomaly Detection in Medical Neuroimaging
Background and mission :
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.
Key words: Machine learning, Deep Learning; Multidimensional data, Segmentation, Neuroimaging, Self-supervised learning,
Anomaly detection, Unsupervised representation learning
Starting date: Autumn 2022
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.
Diplomas required :
N/A
Skills required:
N/A
Compensation :
Depending on curricula
Structure :
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. The PhD position is granted by the “Défi IA” program sponsored by la Région Auvergne Rhône-Alpes.
The position is available in the framework of DAISIES project.