Identifying Suspicious Regions of Covid-19 by Abnormality-Sensitive Activation Mapping
This addresses the need for automated analysis to reduce radiologists' workload in diagnosing COVID-19, but it is incremental as it builds on existing computer-aided diagnosis methods.
The paper tackles the problem of automatically identifying suspicious regions of COVID-19 in chest CT volumes using a 2.5D CNN with 3D attention mechanisms, achieving AUCs over 0.900 and mean sensitivity of 0.853 and specificity of 0.870 across datasets.
This paper presents a fully-automated method for the identification of suspicious regions of a coronavirus disease (COVID-19) on chest CT volumes. One major role of chest CT scanning in COVID-19 diagnoses is identification of an inflammation particular to the disease. This task is generally performed by radiologists through an interpretation of the CT volumes, however, because of the heavy workload, an automatic analysis method using a computer is desired. Most computer-aided diagnosis studies have addressed only a portion of the elements necessary for the identification. In this work, we realize the identification method through a classification task by using a 2.5-dimensional CNN with three-dimensional attention mechanisms. We visualize the suspicious regions by applying a backpropagation based on positive gradients to attention-weighted features. We perform experiments on an in-house dataset and two public datasets to reveal the generalization ability of the proposed method. The proposed architecture achieved AUCs of over 0.900 for all the datasets, and mean sensitivity $0.853 \pm 0.036$ and specificity $0.870 \pm 0.040$. The method can also identify notable lesions pointed out in the radiology report as suspicious regions.