Radiographic images offer an alternative method for the rapid screening and
monitoring of Coronavirus Disease 2019 (COVID-19) patients. This approach is
limited by the shortage of radiology experts who can provide a timely
interpretation of these images. Motivated by this challenge, our paper proposes
a 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 several
architecture improvements meant to boost classification accuracies. First,
DECAPS uses an Inverted Dynamic Routing mechanism which increases model
stability by preventing the passage of information from non-descriptive
regions. Second, DECAPS employs a Peekaboo training procedure which uses a
two-stage patch crop and drop strategy to encourage the network to generate
activation maps for every target concept. The network then uses the activation
maps to focus on regions of interest and combines both coarse and fine-grained
representations of the data. Finally, we use a data augmentation method based
on conditional generative adversarial networks to deal with the issue of data
scarcity. Our model achieves 84.3% precision, 91.5% recall, and 96.1% area
under the ROC curve, significantly outperforming state-of-the-art methods. We
compare the performance of the DECAPS model with three experienced,
well-trained thoracic radiologists and show that the architecture significantly
outperforms them. While further studies on larger datasets are required to
confirm this finding, our results imply that architectures like DECAPS can be
used to assist radiologists in the CT scan mediated diagnosis of COVID-19.

本研究提出了一种名为 “Detail-Oriented Capsule Networks (DECAPS)” 的网络结构，通过结合胶囊网络和多种架构改进，自动诊断放射学影像下的新型冠状病毒肺炎 (COVID-19)。DECAPS 使用倒置动态路由机制、Peekaboo 训练过程和基于条件生成对抗网络的数据增强方法将粗粒度和细粒度表达形式相结合，从而实现自适应特征提取和可解释性高的关注热力图生成。实验结果表明，该模型在诊断性能方面远远优于具有丰富临床经验和训练的放射科医生，同时具有潜在的临床应用和推广价值。