TOURNIAIRE , Paul

The purpose of this thesis is to develop machine learning models that can leverage histology slides and clinical data to predict the outcome of immunotherapies against lung cancer. To this end, there are several challenges to overcome, such as the concurrent classification and localization of information within whole-slide images of large size,...

The purpose of this thesis is to develop machine learning models that can leverage histology slides and clinical data to predict the outcome of immunotherapies against lung cancer. To this end, there are several challenges to overcome, such as the concurrent classification and localization of information within whole-slide images of large size,...

Given the size of digitized Whole Slide Images (WSIs), it is generally laborious and time-consuming for pathologists to exhaustively delineate objects within them, especially with datasets containing hundreds of slides to annotate. Most of the time, only slide-level labels are available, giving rise to the development of weakly-supervised...

Since the standardization of Whole Slide Images (WSIs) digitization, the use of deep learning methods for the analysis of histological images has shown much potential. However, the sheer size of WSIs is a real challenge, as they are often up to 100,000 pixels wide and high at the highest resolution, and therefore cannot be processed directly by...

The histological distinction of lung neuroendocrine carcinoma, including small cell lung carcinoma (SCLC), large cell neuroendocrine carcinoma (LCNEC) and atypical carcinoid (AC), can be challenging in some cases, while bearing prognostic and therapeutic significance. To assist pathologists with the differentiation of histologic subtyping, we...