Report
Histopathology image embedding based on foundation models features aggregation for patient treatment response prediction
| العنوان: | Histopathology image embedding based on foundation models features aggregation for patient treatment response prediction |
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| المؤلفون: | Guetarni, Bilel, Windal, Feryal, Benhabiles, Halim, Chaibi, Mahfoud, Dubois, Romain, Leteurtre, Emmanuelle, Collard, Dominique |
| سنة النشر: | 2024 |
| المجموعة: | Computer Science |
| مصطلحات موضوعية: | Computer Science - Computer Vision and Pattern Recognition |
| الوصف: | 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. Comment: Accepted at MICCAI 2024 workshop MOVI |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/2408.03954 |
| رقم الانضمام: | edsarx.2408.03954 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |