Big Self-Supervised Models Advance Medical Image Classification

• URL: http://arxiv.org/abs/2101.05224v1
• Date: Wed, 13 Jan 2021 17:36:31 GMT
• Title: Big Self-Supervised Models Advance Medical Image Classification
• Authors: Shekoofeh Azizi, Basil Mustafa, Fiona Ryan, Zachary Beaver, Jan Freyberg, Jonathan Deaton, Aaron Loh, Alan Karthikesalingam, Simon Kornblith, Ting Chen, Vivek Natarajan, Mohammad Norouzi
• Abstract summary: We study the effectiveness of self-supervised learning as a pretraining strategy for medical image classification. We use a novel Multi-Instance Contrastive Learning (MICLe) method that uses multiple images of the underlying pathology per patient case. We show that big self-supervised models are robust to distribution shift and can learn efficiently with a small number of labeled medical images.
• Score: 36.23989703428874
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Self-supervised pretraining followed by supervised fine-tuning has seen success in image recognition, especially when labeled examples are scarce, but has received limited attention in medical image analysis. This paper studies the effectiveness of self-supervised learning as a pretraining strategy for medical image classification. We conduct experiments on two distinct tasks: dermatology skin condition classification from digital camera images and multi-label chest X-ray classification, and demonstrate that self-supervised learning on ImageNet, followed by additional self-supervised learning on unlabeled domain-specific medical images significantly improves the accuracy of medical image classifiers. We introduce a novel Multi-Instance Contrastive Learning (MICLe) method that uses multiple images of the underlying pathology per patient case, when available, to construct more informative positive pairs for self-supervised learning. Combining our contributions, we achieve an improvement of 6.7% in top-1 accuracy and an improvement of 1.1% in mean AUC on dermatology and chest X-ray classification respectively, outperforming strong supervised baselines pretrained on ImageNet. In addition, we show that big self-supervised models are robust to distribution shift and can learn efficiently with a small number of labeled medical images.

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