Indirect supervision applied to COVID-19 and pneumonia classification

Abstract

The novel coronavirus 19 (COVID-19) continues to have a devastating effect around the globe, leading many scientists and clinicians to actively seek to develop new techniques to assist with the tackling of this disease. Modern machine learning methods have shown promise in their adoption to assist the healthcare industry through their data and analytics-driven decision making, inspiring researchers to develop new angles to fight the virus. In this paper, we aim to develop a CNN-based method for the detection of COVID-19 by utilizing patients' chest X-ray images. Developing upon the inclusion of convolutional units, the proposed method makes use of indirect supervision based on Grad-CAM. This technique is used in the training process where Grad-CAM's attention heatmaps support the network's predictions. Despite recent progress, scarcity of data has thus far limited the development of a robust solution. We extend upon existing work by combining publicly available data across 5 different sources and carefully annotate the comprising images across three categories: normal, pneumonia, and COVID-19. To achieve a high classification accuracy, we propose a training pipeline based on indirect supervision of traditional classification networks, where the guidance is directed by an external algorithm. With this method, we observed that the widely used, standard networks can achieve an accuracy comparable to tailor-made models, specifically for COVID-19, with one network in particular, VGG-16, outperforming the best of the tailor-made models.

Automatic scoring of COVID-19 severity in X-ray imaging based on a novel deep learning workflow

“…The majority of the current disease classification solutions focus, primarily, on distinguishing whether an infection is present or not, without paying much attention to where the network is looking. Previously 13 , we extended the classification of COVID-19 and pneumonia by utilizing a popular visualization technique known as Grad-CAM 83 . Using Grad-CAM, we validated where the four best-performing networks (MobileNet V2, EfficientNet B1, EfficientNet B3, VGG-16) were focusing, verifying that they are properly looking at the correct patterns in the image and activating around those patterns.…”