POTHER: Patch-Voted Deep Learning-based Chest X-ray Bias Analysis for COVID-19 Detection

Source code related to the article submitted to the International Conference on Computational Science ICCS 2022 in London.

Abstract

A critical step in the fight against COVID-19, which continues to have a catastrophic impact on peoples lives, is the effective screening of patients presented in the clinics with severe COVID-19 symptoms. Chest radiography is one of the promising screening approaches. Many studies reported detecting COVID-19 in chest X-rays accurately using deep learning. A serious limitation of many published approaches is insufficient attention paid to explaining decisions made by deep learning models. Using explainable artificial intelligence methods, we demonstrate that model decisions may rely on confounding factors rather than medical pathology. After an in-depth analysis of potential confounding factors found on chest X-ray images, we propose a novel method to minimise their negative impact. We show that our proposed method is more robust than previous attempts to counter confounding factors like ECG cables in chest X-rays that often influence model classification decisions. In addition to being robust, our method achieves results comparable to the state-of-the-art.

Pre-trained model

Google Drive: POTHER.pt

Disclaimer

Not for medical use. It is not intended for diagnostic use or medical decision-making without review by a clinical professional.