CNN Filter Learning from Drawn Markers for the Detection of Suggestive Signs of COVID-19 in CT Images
This addresses the challenge of limited annotated data for COVID-19 detection in medical imaging, offering a domain-specific solution that is incremental in its approach.
The paper tackles the problem of detecting COVID-19 signs in CT images with scarce annotated data by proposing a method that learns CNN filters from user-drawn markers without backpropagation, achieving a mean accuracy of 0.97 and kappa of 0.93 on a dataset of 117 CT images.
Early detection of COVID-19 is vital to control its spread. Deep learning methods have been presented to detect suggestive signs of COVID-19 from chest CT images. However, due to the novelty of the disease, annotated volumetric data are scarce. Here we propose a method that does not require either large annotated datasets or backpropagation to estimate the filters of a convolutional neural network (CNN). For a few CT images, the user draws markers at representative normal and abnormal regions. The method generates a feature extractor composed of a sequence of convolutional layers, whose kernels are specialized in enhancing regions similar to the marked ones, and the decision layer of our CNN is a support vector machine. As we have no control over the CT image acquisition, we also propose an intensity standardization approach. Our method can achieve mean accuracy and kappa values of $0.97$ and $0.93$, respectively, on a dataset with 117 CT images extracted from different sites, surpassing its counterpart in all scenarios.