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As the COVID-19 pandemic puts pressure on healthcare systems worldwide, thecomputed tomography image based AI diagnostic system has become a sustainablesolution for early diagnosis. However, the model-wise vulnerability underadversarial perturbation hinders its deployment in practical situation. Theexisting adversarial training strategies are difficult to generalized intomedical imaging field challenged by complex medical texture features. Toovercome this challenge, we propose a Contour Attention Preserving (CAP) methodbased on lung cavity edge extraction. The contour prior features are injectedto attention layer via a parameter regularization and we optimize the robustempirical risk with hybrid distance metric. We then introduce a newcross-nation CT scan dataset to evaluate the generalization capability of theadversarial robustness under distribution shift. Experimental results indicatethat the proposed method achieves state-of-the-art performance in multipleadversarial defense and generalization tasks. The code and dataset areavailable at https://github.com/Quinn777/CAP.

Toward Robust Diagnosis: A Contour Attention Preserving Adversarial Defense for COVID-19 Detection