Automatic Grading of Cervical Biopsies by Combining Full and Self-supervision - Mines Paris Accéder directement au contenu
Proceedings/Recueil Des Communications Année : 2023

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


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.
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Dates et versions

hal-04029228 , version 1 (14-03-2023)



Mélanie Lubrano, Tristan Lazard, Guillaume Balezo, Yaëlle Bellahsen-Harrar, Cécile Badoual, et al.. Automatic Grading of Cervical Biopsies by Combining Full and Self-supervision. 13807, Springer Nature Switzerland, pp.408-423, 2023, Lecture Notes in Computer Science, ⟨10.1007/978-3-031-25082-8_27⟩. ⟨hal-04029228⟩
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