Self-Knowledge Distillation based Self-Supervised Learning for Covid-19 Detection from Chest X-Ray Images

Guang Li; Ren Togo; Takahiro Ogawa; Miki Haseyama

ICASSP 2022accepted0 citations

Self-Knowledge Distillation based Self-Supervised Learning for Covid-19 Detection from Chest X-Ray Images

Guang Li, Ren Togo, Takahiro Ogawa, Miki Haseyama

Abstract

The global outbreak of the Coronavirus 2019 (COVID-19) has overloaded worldwide healthcare systems. Computer-aided diagnosis for COVID-19 fast detection and patient triage is becoming critical. This paper proposes a novel self-knowledge distillation based self-supervised learning method for COVID-19 detection from chest X-ray images. Our method can use self-knowledge of images based on similarities of their visual features for self-supervised learning. Experimental results show that our method achieved an HM score of 0.988, an AUC of 0.999, and an accuracy of 0.957 on the largest open COVID-19 chest X-ray dataset.

BibTeX

@inproceedings{icassp2022_selfknowledgedis,
  title = {Self-Knowledge Distillation based Self-Supervised Learning for Covid-19 Detection from Chest X-Ray Images},
  author = {Guang Li and Ren Togo and Takahiro Ogawa and Miki Haseyama},
  booktitle = {ICASSP 2022},
  year = {2022}
}