Self-evolving vision transformer for chest X-ray diagnosis through knowledge distillation.

ABSTRACT: Although deep learning-based computer-aided diagnosis systems have recently achieved expert-level performance, developing a robust model requires large, high-quality data with annotations that are expensive to obtain. This situation poses a conundrum that annually-collected chest x-rays cannot be utilized due to the absence of labels, especially in deprived areas. In this study, we present a framework named distillation for self-supervision and self-train learning (DISTL) inspired by the learning process of the radiologists, which can improve the performance of vision transformer simultaneously with self-supervision and self-training through knowledge distillation. In external validation from three hospitals for diagnosis of tuberculosis, pneumothorax, and COVID-19, DISTL offers gradually

SUBMITTER: Park S 

PROVIDER: S-EPMC9252561 | biostudies-literature | 2022 Jul

REPOSITORIES: biostudies-literature