Automatic Grading of Cervical Biopsies by Combining Full and Self-supervision

Mélanie Lubrano, Tristan Lazard, Guillaume Balezo, Yaëlle Bellahsen-Harrar, Cécile Badoual, Sylvain Berlemont, Thomas Walter

Résultats de recherche: Le chapitre dans un livre, un rapport, une anthologie ou une collection!!Conference contributionRevue par des pairs

Résumé

In computational pathology, predictive models from Whole Slide Images (WSI) mostly rely on Multiple Instance Learning (MIL), where the WSI are represented as a bag of tiles, each of which is encoded by a Neural Network (NN). Slide-level predictions are then achieved by building models on the agglomeration of these tile encodings. The tile encoding strategy thus plays a key role for such models. Current approaches include the use of encodings trained on unrelated data sources, full supervision or self-supervision. While self-supervised learning (SSL) exploits unlabeled data, it often requires large computational resources to train. On the other end of the spectrum, fully-supervised methods make use of valuable prior knowledge about the data but involve a costly amount of expert time. This paper proposes a framework to reconcile SSL and full supervision, showing that a combination of both provides efficient encodings, both in terms of performance and in terms of biological interpretability. On a recently organized challenge on grading Cervical Biopsies, we show that our mixed supervision scheme reaches high performance (weighted accuracy (WA): 0.945), outperforming both SSL (WA: 0.927) and transfer learning from ImageNet (WA: 0.877). We further shed light upon the internal representations that trigger classification results, providing a method to reveal relevant phenotypic patterns for grading cervical biopsies. We expect that the combination of full and self-supervision is an interesting strategy for many tasks in computational pathology and will be widely adopted by the field.

langue originaleAnglais
titreComputer Vision – ECCV 2022 Workshops, Proceedings
rédacteurs en chefLeonid Karlinsky, Tomer Michaeli, Ko Nishino
EditeurSpringer Science and Business Media Deutschland GmbH
Pages408-423
Nombre de pages16
ISBN (imprimé)9783031250811
Les DOIs
étatPublié - 1 janv. 2023
Modification externeOui
Evénement17th European Conference on Computer Vision, ECCV 2022 - Tel Aviv, Israël
Durée: 23 oct. 202227 oct. 2022

Série de publications

NomLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume13807 LNCS
ISSN (imprimé)0302-9743
ISSN (Electronique)1611-3349

Une conférence

Une conférence17th European Conference on Computer Vision, ECCV 2022
Pays/TerritoireIsraël
La villeTel Aviv
période23/10/2227/10/22

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