Histopathology image embedding based on foundation models features aggregation for patient treatment response prediction

• URL: http://arxiv.org/abs/2408.03954v1
• Date: Tue, 23 Jul 2024 13:31:12 GMT
• Title: Histopathology image embedding based on foundation models features aggregation for patient treatment response prediction
• Authors: Bilel Guetarni, Feryal Windal, Halim Benhabiles, Mahfoud Chaibi, Romain Dubois, Emmanuelle Leteurtre, Dominique Collard,
• Abstract summary: 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. Our experimental study conducted on a dataset of 152 patients, shows the promising results of our methodology.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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