Short bio
Master 2 Mathematics and applications, UPMC
Thesis title
Deep learning for digital pathology: from full to no supervision.
Short abstract
Digital pathology involves studying digital versions of tissue slides to make diagnoses or derive prognostic markers from cells and tissue features. Deep learning can automate some diagnostic steps or help discover associations between phenotype and genotype. However, these slides are large, with full magnification versions weighing up to 16GB uncompressed, which therefore raises specific challenges. The goal of this PhD is to determine the best supervision methods to extract information from these slides.