低剂量CT恢复的对抗鲁棒性评估
Evaluating Adversarial Robustness of Low dose CT Recovery
AI总结：
该研究评估了低剂量CT恢复中深度学习与经典方法的对抗鲁棒性，发现深度网络更易受无目标攻击影响，局部攻击可改变病灶外观且保持高数据一致性，可用于探索解空间。
摘要：
使用降低辐射或稀疏角度测量的低剂量计算机断层扫描（CT）采集被推荐用于减少X射线辐射的有害影响。近期的研究成功将深度网络应用于基准数据集上的低剂量CT恢复问题。然而，在应用于临床环境之前，需要对其鲁棒性进行全面评估。在这项工作中，我们评估了不同深度学习方法和经典方法在CT恢复中的鲁棒性。我们表明，包括鼓励数据一致性的基于模型的网络在内的深度网络，更容易受到无目标攻击的影响。令人惊讶的是，我们观察到，即使对于这些质量较差的重建，数据一致性也没有受到严重影响，这促使需要为网络提供更好的正则化。我们证明了通用攻击的可行性，并研究了不同方法之间的攻击可迁移性。我们分析了针对临床相关区域引起局部变化的攻击的鲁棒性。经典方法和深度网络都受到此类攻击的影响，在极小扰动下导致局部病灶视觉外观的改变。由于所得重建与原始测量具有高数据一致性，这些局部攻击可用于探索CT恢复问题的解空间。
英文摘要：
Low dose computed tomography (CT) acquisition using reduced radiation or sparse angle measurements is recommended to decrease the harmful effects of X-ray radiation. Recent works successfully apply deep networks to the problem of low dose CT recovery on bench-mark datasets. However, their robustness needs a thorough evaluation before use in clinical settings. In this work, we evaluate the robustness of different deep learning approaches and classical methods for CT recovery. We show that deep networks, including model-based networks encouraging data consistency, are more susceptible to untargeted attacks. Surprisingly, we observe that data consistency is not heavily affected even for these poor quality reconstructions, motivating the need for better regularization for the networks. We demonstrate the feasibility of universal attacks and study attack transferability across different methods. We analyze robustness to attacks causing localized changes in clinically relevant regions. Both classical approaches and deep networks are affected by such attacks leading to changes in the visual appearance of localized lesions, for extremely small perturbations. As the resulting reconstructions have high data consistency with the original measurements, these localized attacks can be used to explore the solution space of the CT recovery problem.
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