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

• URL: http://arxiv.org/abs/2202.06431v1
• Date: Sun, 13 Feb 2022 22:40:46 GMT
• Title: AI can evolve without labels: self-evolving vision transformer for chest X-ray diagnosis through knowledge distillation
• Authors: Sangjoon Park, Gwanghyun Kim, Yujin Oh, Joon Beom Seo, Sang Min Lee, Jin Hwan Kim, Sungjun Moon, Jae-Kwang Lim, Chang Min Park, and Jong Chul Ye
• Abstract summary: We present a novel deep learning framework that uses knowledge distillation through self-supervised learning and self-training. Experimental results show that the proposed framework maintains impressive robustness against a real-world environment. The proposed framework has a great potential for medical imaging, where plenty of data is accumulated every year.
• Score: 30.075714642990768
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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.

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