Self supervised contrastive learning for digital histopathology

Understand

Unsupervised learning has been a long-standing goal of machine learning and is especially important for medical image analysis, where the learning can compensate for the scarcity of labeled datasets.

• A promising subclass of unsupervised learning is self-supervised learning, which aims to learn salient features using the raw input as the learning signal.
• In this paper, we use a contrastive self-supervised learning method called SimCLR that achieved state-of-the-art results on natural-scene images and apply this method to digital histopathology by collecting and pretraining on 57 histopathology datasets without any labels.
• We find that combining multiple multi-organ datasets with different types of staining and resolution properties improves the quality of the learned features.