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

• DOI: 10.48550/arxiv.2202.06431
• Publication Date: 2022-01-01
• Authors: Park, Sangjoon; Kim, Gwanghyun; Oh, Yujin; Seo, Joon Beom; Lee, Sang Min; Kim, Jin Hwan; Moon, Sungjun; Lim, Jae-Kwang; Park, Chang Min; Ye, Jong Chul
• Keywords: FOS: Computer and information sciences; Computer Science - Machine Learning; Computer Vision and Pattern Recognition (cs.CV); Image and Video Processing (eess.IV); Computer Science - Computer Vision and Pattern Recognition; FOS: Electrical engineering, electronic engineering, information engineering; Electrical Engineering and Systems Science - Image and Video Processing; 3. Good health; Machine Learning (cs.LG)

Although deep learning-based computer-aided diagnosis systems have recently achieved expert-level performance, developing a robust deep learning model requires large, high-quality data with manual annotation, which is expensive to obtain. This situation poses the problem that the chest x-rays collected annually in hospitals cannot be used due to the lack of manual labeling by experts, especially in deprived areas. To address this, here we present a novel deep learning framework that uses knowledge distillation through self-supervised learning and self-training, which shows that the performance of the original model trained with a small number of labels can be gradually improved with more unlabeled data. Experimental results show that the proposed framework maintains impressive robustness against a real-world environment and has general applicability to several diagnostic tasks such as tuberculosis, pneumothorax, and COVID-19. Notably, we demonstrated that our model performs even better than those trained with the same amount of labeled data. The proposed framework has a great potential for medical imaging, where plenty of data is accumulated every year, but ground truth annotations are expensive to obtain.<br/>24 pages<br/>