Self-Supervised Learning for Histopathology Image Analysis with Limited Labels

The histopathology image analysis is very important in correct diagnosis of diseases especially of cancer. Nonetheless, it is difficult to create high-performance deep learning models to perform this task because there is not a high abundance of available labelled datasets since manual annotation of expert pathologists is time-consuming and resource-intensive. This paper experiments with the application of self-supervised learning (SSL) in enhancing the analysis of histopathology images in shadowed data set conditions. Particularly, convolutional neural networks were trained in advance on challenges of large sets of unlabeled histopathology slides via contrastive learning and pretext tasks including patch prediction and image reconstruction. The learned feature representations were then refined with the help of a small annotated sample size to do downstream classification as well as segmentation. The results of the experimental evidence prove the more convincing effect of the models based on proposing the SSL framework as compared to those based on fully supervised baselines, where the accuracy of such models and their better generalization is observed in case of limited labelled data. Plotting of stored learned feature embeddings also demonstrations of better segregation between tissue categories suggest that the model is successful in capturing discriminative histopathological patterns. In general, the solution suggested minimizes the use of a large manual labelling effort and offers a production-scalable and stable solution of computational pathology within limited databases of clinical settings.