Deep learning for medical imaging-driven diagnosis models. Application to neuropathologies.

When:
15/09/2019 – 16/09/2019 all-day
2019-09-15T02:00:00+02:00
2019-09-16T02:00:00+02:00

Annonce en lien avec l’Action/le Réseau : aucun

Laboratoire/Entreprise : Laboratoire CREATIS – UMR CNRS 5220
Durée : 36 mois
Contact : carole.lartizien@creatis.insa-lyon.fr
Date limite de publication : 2019-09-15

Contexte :
In recent years, deep machine learning has received a lot of attention to explore and structure multidimensional and multimodality medical imaging data. The ’Images and Models’ team from CREATIS lab (www.creatis.insalyon.fr) in Lyon (France) has developed a strong expertise in the domain of machine learning for the design of diagnosis and prognosis models.

Sujet :
The objective of this PhD project is to perform upstream research in deep learning to push the performance limits of the prototype models we have designed so far.

The PhD candidate will benefit from ongoing collaborations with external experts in the machine learning domain, neurologists from Hospices Civils de Lyon (HCL), as well as the scientific emulating environment of the ’Images and
Models’ team which currently explores the potential of machine and deep learning for medical image processing.

A detailed description of the project is available here : https://www.creatis.insa-lyon.fr/site7/fr/node/46826

Profil du candidat :
We are looking for an enthusiastic and autonomous student with strong motivation and interest in multidisciplinary research.

Formation et compétences requises :
The candidate is expected to have strong knowledge either in machine learning or image processing and a good experience in both fields. Some prior experience with medical image processing would be appreciated but is not required. Good programming skills are also required.

Adresse d’emploi :
CREATIS laboratory (www.creatis.insa-lyon.fr)
INSA Lyon
Bâtiment Blaise Pascal
7 avenue Jean Capelle
69621 Villeurbanne
France

Document attaché : PhD_CREATIS_DeepLearning_April2019.pdf