Twin self-supervision based semi-supervised learning (TS-SSL): Retinal anomaly classification in SD-OCT images

The performance of supervised deep learning significantly relies on the volume of training samples. However, the vast majority of medical images lacks manual expert annotations. Compared to natural image annotation, the cost of medical image annotation is more expensive as it requires professional medical knowledge guidance. To tackle the predicament, semi-supervised learning and self-supervised learning are very effective technologies. In this paper, we present a twin self-supervision based semi-supervised learning (TS-SSL) approach that embeds two types of self-supervised strategies (namely generative self-supervised learning and discriminative self-supervised learning) into semi-supervised framework to simultaneously learn from few-shot labeled images and vast unlabeled images. TS-SSL is an end-to-end classification model, in which semi-supervision and self-supervision can be jointly trained. The proposed TS-SSL is applied to perform retinal anomaly classification based on spectral-domain optical coherence tomography (SD-OCT) images. The experiments demonstrate that TS-SSL yields the good classification performance on one public SD-OCT dataset and two private SD-OCT datasets with only 10% labels. We also claim that TS-SSL can be transferred to other medical imaging modalities. The code is available at https://github.com/ZhangYH0502/TS-SSL.