Self-supervised learning for the detection of anomalies in neuroimaging. Application to the early diagnosis of Parkinson's disease
Recrutement: 
Recrutement en cours/passé: 
Recrutement en cours
Periode: 
2021-2022
Contact: 
carole.lartizien@creatis.insa-lyon.fr nicolas.pinon@creatis.insa-lyon.fr

See a detailled description in the attached pdf (english and french versions)

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Host laboratory : Laboratoire CREATIS, 69 Villeurbanne- MYRIAD Team

Supervisors : Carole Lartizien - carole.lartizien@creatis.insa-lyon.fr

                     Nicolas Pinon - nicolas.pinon@creatis.insa-lyon.fr

Keywords : Medical Image analysis and Modeling, Deep Learning, self-supervised supervised learning, Neuroimaging

Duration : 5-6 months.

Starting date : feb-april 2022

Gratuity  : ~560 euros/month

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The vast majority of deep architectures for medical image analysis are based on supervised methods requiring the collection of large datasets of annotated examples. Building such annotated datasets is hardly achievable, especially for some specific tasks, including the detection of small and subtle lesions, which are sometimes impossible to visually detect and thus manually outline. This is the case for various brain pathologies including Parkinson's disease.

We  have developed an expertise in the field of anomaly detection methods for the analysis of multi-modality brain images, and recently applied it to the detection of early forms of Parkinson's disease in Parkinson's disease in multiparametric MRI, in collaboration with the Centre de Neurosciences (GIN) and INRIA Grenoble.

The purpose of this master project is to improve the  performance achieved with the current model architecture by exploring methodological research axes in the domains of deep latent representation learning and visualisation (see attached pdf file for details). 

The successful candidate will have access to the PPMI database (https://www.ppmi-info.org/accessdata-specimens/download-data) containing multiple images of controls and parkinsonian patients in different modalities and as well as to computing resources (CREATIS and/or CNRS supercomputer).

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

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). The candidate will also have the opportunity to interact with a PhD student working on this project.