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Although deep learning-based computer-aided diagnosis systems have recentlyachieved expert-level performance, developing a robust deep learning modelrequires large, high-quality data with manual annotation, which is expensive toobtain. This situation poses the problem that the chest x-rays collectedannually in hospitals cannot be used due to the lack of manual labeling byexperts, especially in deprived areas. To address this, here we present a noveldeep learning framework that uses knowledge distillation throughself-supervised learning and self-training, which shows that the performance ofthe original model trained with a small number of labels can be graduallyimproved with more unlabeled data. Experimental results show that the proposedframework maintains impressive robustness against a real-world environment andhas general applicability to several diagnostic tasks such as tuberculosis,pneumothorax, and COVID-19. Notably, we demonstrated that our model performseven better than those trained with the same amount of labeled data. Theproposed framework has a great potential for medical imaging, where plenty ofdata is accumulated every year, but ground truth annotations are expensive toobtain.

AI can evolve without labels: self-evolving vision transformer for chest X-ray diagnosis through knowledge distillation