Date depot: 22 mars 2021 Titre: Digital Histopathology and Micro Tumoral Environment Analysis and Exploration Directeur de thèse: Nicolas LOMENIE (LIPADE) Encadrant : Camille KURTZ (LIPADE) Domaine scientifique: Sciences pour l'ingénieur Thématique CNRS : Non defini Resumé: The rapidly emerging field of computational pathology needs new paradigms and tools coming from computer sciences in the broadest sense of the science. Our team is developing many projects in the field including setting up international challenges (i.e. serving as Scientific Committee president of https://www.drivendata.org/competitions/67/competition-cervical-biopsy/ : TissueNet : detect lesions in cervical biopsies hosted by French Society of Pathology and Health Data Hub). The ultimate goal is to be able to provide objective diagnosis, therapeutic response prediction and identification of new morphological features of clinical relevance within the next decade. Deep learning is at the core of a new revolution in data science. Yet, deep learning-based computational pathology approaches either require manual annotation of gigapixel whole slide images (WSIs) in fully-supervised settings or thousands of WSIs with slide-level labels in a weakly-supervised setting. A WSI is worth up to 10 GBytes of data (one WSIA = one image/ one patient/ one exam). The team has a pending patent in the field and is part of a phase 2 clinical trial for immuno-therapy as a digital companion test. We are involved in the community of the French Society of Pathology. We believe that the next step will rely on the integration of the computer vision/machine learning pipelines into the clinical setting.