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XDeepCell - Research project

Explainable deep models in cell imaging: application to the analysis of structural changes in human cells for diagnostic purpose

PhD student: Martin BLANCHARD

ABSTRACT

The biological function of a cell is very often associated with its morphology. For example, in the case of human podocytes (cells involved in renal glomerular filtration), the morphological modifications observed on in vitro cultures are associated with a modification of the permeability and allow to quantify the toxicity level of a drug. This project proposes to jointly address first a problem of morphological characterization from biology and a computer vision problem based on advanced deep learning methods. On the biology side, the aim is to understand the structural modifications of cells after treatment (known drugs or biological samples of interest) and to propose a method of statistical quantitative characterization allowing to evaluate the degree of functional damage linked to the toxicity or to the considered pathology. In terms of computer vision, the aim is to propose explainable models for representation learning in a context of poorly annotated data. Indeed, in the context of the toxicity study, we have access to a global annotation of the image while the cells are not systematically all affected by the treatment.