Self-Knowledge Distillation based Self-Supervised Learning for Covid-19 Detection from Chest X-Ray Images
This addresses the problem of fast COVID-19 diagnosis for healthcare systems, but it is incremental as it applies a novel method to a specific medical imaging task.
The paper tackled COVID-19 detection from chest X-ray images using a self-knowledge distillation based self-supervised learning method, achieving an HM score of 0.988, an AUC of 0.999, and an accuracy of 0.957 on the largest open dataset.
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.