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arXiv · 2206.03009

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

Guang Li·Ren Togo·Takahiro Ogawa·Miki Haseyama
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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.

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Guang Li, Ren Togo, Takahiro Ogawa, Miki Haseyama. 2022-06-07. Self-Knowledge Distillation based Self-Supervised Learning for Covid-19 Detection from Chest X-Ray Images. https://doi.org/10.1109/icassp43922.2022.9746540

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