Researchers seek help from deep learning methods to alleviate the enormous burden of reading radiological images by clinicians during the COVID-19 pandemic. However, clinicians are often reluctant to trust deep models due to their black-box characteristics. To automatically differentiate COVID-19 and community-acquired pneumonia from healthy lungs in radiographic imaging, we propose an explainable attention-transfer classification model based on the knowledge distillation network structure. The attention transfer direction always goes from the teacher network to the student network. Firstly, the teacher network extracts global features and concentrates on the infection regions to generate attention maps. It uses a deformable attention module to strengthen the response of infection regions and to suppress noise in irrelevant regions with an expanded reception field. Secondly, an image fusion module combines attention knowledge transferred from teacher network to student network with the essential information in original input. While the teacher network focuses on global features, the student branch focuses on irregularly shaped lesion regions to learn discriminative features. Lastly, we conduct extensive experiments on public chest X-ray and CT datasets to demonstrate the explainability of the proposed architecture in diagnosing COVID-19.

中文翻译：

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利用放射线成像进行 COVID-19 自动诊断：可解释的注意力转移深度神经网络


研究人员寻求深度学习方法的帮助，以减轻临床医生在 COVID-19 大流行期间阅读放射图像的巨大负担。然而，由于深度模型的黑盒特性，临床医生往往不愿意信任它们。为了在放射成像中自动区分 COVID-19 和社区获得性肺炎与健康肺部，我们提出了一种基于知识蒸馏网络结构的可解释的注意力转移分类模型。注意力转移方向总是从教师网络到学生网络。首先，教师网络提取全局特征并集中于感染区域以生成注意力图。它使用可变形注意模块来加强感染区域的响应，并通过扩大接收场来抑制不相关区域中的噪声。其次，图像融合模块将从教师网络转移到学生网络的注意力知识与原始输入中的基本信息相结合。教师网络专注于全局特征，而学生分支则专注于不规则形状的病变区域以学习判别特征。最后，我们对公共胸部 X 射线和 CT 数据集进行了广泛的实验，以证明所提出的架构在诊断 COVID-19 方面的可解释性。