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Radiographic images offer an alternative method for the rapid screening andmonitoring of Coronavirus Disease 2019 (COVID-19) patients. This approach islimited by the shortage of radiology experts who can provide a timelyinterpretation of these images. Motivated by this challenge, our paper proposesa novel learning architecture, called Detail-Oriented Capsule Networks(DECAPS), for the automatic diagnosis of COVID-19 from Computed Tomography (CT)scans. Our network combines the strength of Capsule Networks with severalarchitecture improvements meant to boost classification accuracies. First,DECAPS uses an Inverted Dynamic Routing mechanism which increases modelstability by preventing the passage of information from non-descriptiveregions. Second, DECAPS employs a Peekaboo training procedure which uses atwo-stage patch crop and drop strategy to encourage the network to generateactivation maps for every target concept. The network then uses the activationmaps to focus on regions of interest and combines both coarse and fine-grainedrepresentations of the data. Finally, we use a data augmentation method basedon conditional generative adversarial networks to deal with the issue of datascarcity. Our model achieves 84.3% precision, 91.5% recall, and 96.1% areaunder the ROC curve, significantly outperforming state-of-the-art methods. Wecompare the performance of the DECAPS model with three experienced,well-trained thoracic radiologists and show that the architecture significantlyoutperforms them. While further studies on larger datasets are required toconfirm this finding, our results imply that architectures like DECAPS can beused to assist radiologists in the CT scan mediated diagnosis of COVID-19.

Radiologist-Level COVID-19 Detection Using CT Scans with Detail-Oriented Capsule Networks