Region of interest discovery using discriminative concrete autoencoder for COVID-19 lung CT images

The coronavirus pandemic, also known as COVID-19 pandemic, has led to tens of millions of cases and over half of a million deaths as of August 2020. Chest CT is an important imaging tool to evaluate the severity of the lung involvement which often correlates with the severity of the disease. Quantitative analysis of CT lung images requires the localization of the infection area on the image or the identification of the region of interest (ROI). In this study, we propose an automatic ROI identification based on the recent feature selection method, called concrete autoencoder, that learns the parameters of concrete distributions from the given data to choose pixels from the images. To improve the discrimination of these features, we proposed a discriminative concrete autoencoder (DCA) by adding a classification head to network. This classification head is used to perform the image classification. We conducted a study with 30 CT image sets from 15 Covid-19 positive and 15 COVID19 negative cases. When we used the DCA to select the pixels of the suspected area, the classification accuracy was 76.27% for the image sets. Without DCA feature selection, the traditional neural network achieved an accuracy of 69.41% for the same image sets. Hence, the proposed DCA could detect significant features to identify the COVID-19 infected area of lung. Future work will focus on surveying more data, designing area selection layer towards group selection.