  Predicting the response of a patient to a cancer treatment is of high
interest. Nonetheless, this task is still challenging from a medical point of
view due to the complexity of the interaction between the patient organism and
the considered treatment. Recent works on foundation models pre-trained with
self-supervised learning on large-scale unlabeled histopathology datasets have
opened a new direction towards the development of new methods for cancer
diagnosis related tasks. In this article, we propose a novel methodology for
predicting Diffuse Large B-Cell Lymphoma patients treatment response from Whole
Slide Images. Our method exploits several foundation models as feature
extractors to obtain a local representation of the image corresponding to a
small region of the tissue, then, a global representation of the image is
obtained by aggregating these local representations using attention-based
Multiple Instance Learning. Our experimental study conducted on a dataset of
152 patients, shows the promising results of our methodology, notably by
highlighting the advantage of using foundation models compared to conventional
ImageNet pre-training. Moreover, the obtained results clearly demonstrates the
potential of foundation models for characterizing histopathology images and
generating more suited semantic representation for this task.
