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Une approche pour regrouper des sujets atteints de cancers, sur la base des similarités des répartitions cellulaires au sein de leurs biopsies

Abstract : In this paper, we introduce a novel and interpretable methodology to cluster subjects suffering from cancer, based on features extracted from their biopsies. Contrary to existing approaches, we propose here to capture complex patterns in the repartitions of their cells using histograms, and compare subjects on the basis of these repartitions. We describe here our complete workflow, including creation of the database, cells segmentation and phenotyping, computation of complex features, choice of a distance function between features, clustering between subjects using that distance, and survival analysis of obtained clusters. We illustrate our approach on a database of hematoxylin and eosin (H&E)-stained tissues of subjects suffering from Stage I lung adenocarcinoma, where our results match existing knowledge in prognosis estimation with high confidence.
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https://hal-imt-atlantique.archives-ouvertes.fr/hal-03131445
Contributor : Bastien Pasdeloup <>
Submitted on : Thursday, February 4, 2021 - 1:49:08 PM
Last modification on : Wednesday, July 21, 2021 - 7:38:03 AM
Long-term archiving on: : Wednesday, May 5, 2021 - 6:55:29 PM

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  • HAL Id : hal-03131445, version 1

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Yassine El Ouahidi, Matis Feller, Matthieu Talagas, Bastien Pasdeloup. Une approche pour regrouper des sujets atteints de cancers, sur la base des similarités des répartitions cellulaires au sein de leurs biopsies. EGC 2021 : 21ème conférence Extraction et Gestion des Connaissances, Jan 2021, Montpellier, France. pp.333-340. ⟨hal-03131445⟩

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