  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.
