Histopathology image embedding based on foundation models features aggregation for patient treatment response prediction
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arXiv
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| Autores principales: | , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| _version_ | 1866929452308496384 |
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| author | Guetarni, Bilel Windal, Feryal Benhabiles, Halim Chaibi, Mahfoud Dubois, Romain Leteurtre, Emmanuelle Collard, Dominique |
| author_facet | Guetarni, Bilel Windal, Feryal Benhabiles, Halim Chaibi, Mahfoud Dubois, Romain Leteurtre, Emmanuelle Collard, Dominique |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_03954 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Histopathology image embedding based on foundation models features aggregation for patient treatment response prediction Guetarni, Bilel Windal, Feryal Benhabiles, Halim Chaibi, Mahfoud Dubois, Romain Leteurtre, Emmanuelle Collard, Dominique Computer Vision and Pattern Recognition 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. |
| title | Histopathology image embedding based on foundation models features aggregation for patient treatment response prediction |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2408.03954 |