Self-supervised Learning for COVID-19 Detection from Chest X-ray Images

Most of existing computer vision applications rely on models trained on supervised corpora, this is contradictory to what the world is seeing with the explosion of massive sets of unlabeled data. In the field of medical imaging for example, creating labels is extremely time-consuming because professionals should spend countless hours looking at images to manually annotate, segment, etc. Recently, several works are looking for solutions to the challenge of learning effective visual representations with no human supervision. In this work, we investigate the potential of using a self-supervised learning as a pretraining phase in improving the classification of radiographic images when the amount of available annotated data is small. To do that, we propose to use a self-supervised framework by pretraining a deep encoder with contrastive learning on a chest X-ray dataset using no labels at all, and then fine-tuning it using only few labeled data samples. We experimentally demonstrate that an unsupervised pretraining on unlabeled data is able to learn useful representation from Chest X-ray images, and only few labeled data samples are sufficient to reach the same accuracy of a supervised model learnt on the whole annotated dataset.