As the COVID-19 pandemic puts pressure on healthcare systems worldwide, the
computed tomography image based AI diagnostic system has become a sustainable
solution for early diagnosis. However, the model-wise vulnerability under
adversarial perturbation hinders its deployment in practical situation. The
existing adversarial training strategies are difficult to generalized into
medical imaging field challenged by complex medical texture features. To
overcome this challenge, we propose a Contour Attention Preserving (CAP) method
based on lung cavity edge extraction. The contour prior features are injected
to attention layer via a parameter regularization and we optimize the robust
empirical risk with hybrid distance metric. We then introduce a new
cross-nation CT scan dataset to evaluate the generalization capability of the
adversarial robustness under distribution shift. Experimental results indicate
that the proposed method achieves state-of-the-art performance in multiple
adversarial defense and generalization tasks. The code and dataset are
available at this https URL

提出了一种基于肺部空腔边缘提取的轮廓注意力保持方法来提高由于医学复杂纹理特征挑战而难以泛化的医学成像领域中的计算机断层扫描图像 AI 诊断系统的鲁棒性和推广能力。