Multi-scale alignment and Spatial ROI Module for COVID-19 Diagnosis
This work addresses the need for improved computer-aided diagnosis tools to help doctors effectively screen for COVID-19, but it appears incremental as it builds on existing deep learning techniques with specific modules for this domain.
The paper tackled the problem of detecting COVID-19 lesions in CT and CXR images, where infected areas are small and prone to omission by standard deep learning methods, by proposing a D-SPP module for multi-scale information extraction and a CID module for focusing on lesion areas, resulting in higher accuracy on four datasets.
Coronavirus Disease 2019 (COVID-19) has spread globally and become a health crisis faced by humanity since first reported. Radiology imaging technologies such as computer tomography (CT) and chest X-ray imaging (CXR) are effective tools for diagnosing COVID-19. However, in CT and CXR images, the infected area occupies only a small part of the image. Some common deep learning methods that integrate large-scale receptive fields may cause the loss of image detail, resulting in the omission of the region of interest (ROI) in COVID-19 images and are therefore not suitable for further processing. To this end, we propose a deep spatial pyramid pooling (D-SPP) module to integrate contextual information over different resolutions, aiming to extract information under different scales of COVID-19 images effectively. Besides, we propose a COVID-19 infection detection (CID) module to draw attention to the lesion area and remove interference from irrelevant information. Extensive experiments on four CT and CXR datasets have shown that our method produces higher accuracy of detecting COVID-19 lesions in CT and CXR images. It can be used as a computer-aided diagnosis tool to help doctors effectively diagnose and screen for COVID-19.