Analyse par apprentissage profond d’images histopathologiques à haute résolution et quantité limitée: cas d’usage pour le sous-typage et la réponse au traitement du lymphome diffus
Bilel Guetarni · HAL (Le Centre pour la Communication Scientifique Directe) · 2024
Non-Hodgkin’s lymphomas (NHL) are cancers of the immune system, and are responsible for more than 250,000 deaths in 2022. Diffuse large B-cell lymphoma (DLBCL) is the most common type of NHL, with two molecular subtypes identified using gene expression profiling techniques: ABC and GCB, the former of which is characterized by a lower survival rate and the need for specific treatments. Several methods exist to determine the DLBCL subtype, nevertheless, these methods have limitations in terms of cost, time and accuracy. In this thesis, we propose to investigate the potential of machine learning and deep learning-based methods to improve the diagnosis of patients with DLBCL, both in terms of molecular subtyping but also for the prediction of treatment response. Thanks to the acquisition of high-resolution histopathological images, we propose two methodologies leading to models capable of predicting a patient’s molecular subtype and treatment response from these images. The experimental results of these works have demonstrated the potential contribution of these methods to the diagnosis of DLBCL, but also their ability to be generalized to other types of cancer and predictive tasks.