A Cascaded Learning Strategy for Robust COVID-19 Pneumonia Chest X-Ray Screening
This work addresses the challenge of reliable COVID-19 screening from chest X-rays for healthcare applications, but it is incremental as it builds on existing deep learning methods with a novel training strategy.
The paper tackles the problem of limited public chest X-ray data for COVID-19 detection by proposing a cascaded learning strategy that leverages a large dataset of non-COVID-19 pneumonia images to improve model generalization, achieving good classification performance on an expanded dataset including COVID-19 cases.
We introduce a comprehensive screening platform for the COVID-19 (a.k.a., SARS-CoV-2) pneumonia. The proposed AI-based system works on chest x-ray (CXR) images to predict whether a patient is infected with the COVID-19 disease. Although the recent international joint effort on making the availability of all sorts of open data, the public collection of CXR images is still relatively small for reliably training a deep neural network (DNN) to carry out COVID-19 prediction. To better address such inefficiency, we design a cascaded learning strategy to improve both the sensitivity and the specificity of the resulting DNN classification model. Our approach leverages a large CXR image dataset of non-COVID-19 pneumonia to generalize the original well-trained classification model via a cascaded learning scheme. The resulting screening system is shown to achieve good classification performance on the expanded dataset, including those newly added COVID-19 CXR images.