Annonce en lien avec l’Action/le Réseau : aucun
Laboratoire/Entreprise : ICube UMR 7357 (Strasbourg)
Durée : 34 months
Contact : wemmert@unistra.fr
Date limite de publication : 2020-03-01
Contexte :
The AiCOLO project aims to contribute to the development of new artificial intelligence techniques trained on a large cohort of clinical annotated colon cancer patients with a twofold objective.
On the one hand, we will develop an innovative prognostic aid tool to automatically classify tissues constituting WSI pathological slides and to enumerate the various known prognostic markers like TILS immune infiltration, stromal contain or eosinophil count in the different areas. The method will be trained to find image patterns in tumor tissue related to patients’ outcome.
On the other hand, we will also propose a resolutely new approach to predict RAS and BRAF genetic status from WSI. We aim to determine if artificial intelligence could detect patterns associated with such genetic features and could outperform clinical or immune infiltrate variables.The idea is to study the activation layers of a deep network trained to classify the patients in order to extract information to explain its decision.
Sujet :
The objective is to develop a complete methodology enabling to assign a label to each image region. This problem will be tackled by two complementary approaches: a pixel-based method, in order to obtain a cartography of regions of interest for the studied pathologies (inflammatory zones or tumors area) and an object-based approach enabling to compute a list of biological objects with their contour, localization and attributes. The main workflow must be automatic in order to operate in an unsupervised manner, which constitutes a crucial and challenging aspect of this task.
Profil du candidat :
PhD in computer science
Formation et compétences requises :
PhD in computer science
Skills in:
– Machine learning
– Deep learning
– Image analysis
– Python programming
Adresse d’emploi :
ICube UMR 7357
300 bd Sébastien Brant
F-67412 Illkirch
France
Document attaché : 2019___AiCOLO_postdoc_position_compressed.pdf