Self supervised learning improves dMMR/MSI detection from histology slides across multiple cancers

• URL: http://arxiv.org/abs/2109.05819v1
• Date: Mon, 13 Sep 2021 09:43:12 GMT
• Title: Self supervised learning improves dMMR/MSI detection from histology slides across multiple cancers
• Authors: Charlie Saillard, Olivier Dehaene, Tanguy Marchand, Olivier Moindrot, Aur\'elie Kamoun, Benoit Schmauch, Simon Jegou
• Abstract summary: Microsatellite instability (MSI) is a tumor phenotype whose diagnosis largely impacts patient care in colorectal cancers (CRC) Deep learning models detecting MSI tumors directly from H&E stained slides have shown promise in improving diagnosis of MSI patients. We leverage recent advances in self-supervised learning by training neural networks on histology images from the TCGA dataset using MoCo V2.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Microsatellite instability (MSI) is a tumor phenotype whose diagnosis largely impacts patient care in colorectal cancers (CRC), and is associated with response to immunotherapy in all solid tumors. Deep learning models detecting MSI tumors directly from H&E stained slides have shown promise in improving diagnosis of MSI patients. Prior deep learning models for MSI detection have relied on neural networks pretrained on ImageNet dataset, which does not contain any medical image. In this study, we leverage recent advances in self-supervised learning by training neural networks on histology images from the TCGA dataset using MoCo V2. We show that these networks consistently outperform their counterparts pretrained using ImageNet and obtain state-of-the-art results for MSI detection with AUCs of 0.92 and 0.83 for CRC and gastric tumors, respectively. These models generalize well on an external CRC cohort (0.97 AUC on PAIP) and improve transfer from one organ to another. Finally we show that predictive image regions exhibit meaningful histological patterns, and that the use of MoCo features highlighted more relevant patterns according to an expert pathologist.

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