In recent years, medical images have significantly improved and facilitated diagnosis in versatile tasks including classification of lung diseases, detection of nodules, brain tumor segmentation, and body organs recognition. On the other hand, the superior performance of machine learning (ML) techniques, specifically deep learning networks (DNNs), in various domains has lead to the application of deep learning approaches in medical image classification and segmentation. Due to the security and vital issues involved, healthcare systems are considered quite challenging and their performance accuracy is of great importance. Previous studies have shown lingering doubts about medical DNNs and their vulnerability to adversarial attacks. Although various defense methods have been proposed, there are still concerns about the application of medical deep learning approaches. This is due to some of medical imaging weaknesses, such as lack of sufficient amount of high quality images and labeled data, compared to various high-quality natural image datasets. This paper reviews recently proposed adversarial attack methods to medical imaging DNNs and defense techniques against these attacks. It also discusses different aspects of these methods and provides future directions for improving neural network’s robustness.

近年来，医学图像在肺部疾病分类、结节检测、脑肿瘤分割和身体器官识别等多种任务中得到了显着改善和促进诊断。另一方面，机器学习 (ML) 技术，特别是深度学习网络 (DNN) 在各个领域的卓越性能导致深度学习方法在医学图像分类和分割中的应用。由于涉及的安全和重要问题，医疗保健系统被认为具有相当大的挑战性，其性能准确性非常重要。先前的研究表明，人们对医学 DNN 及其对对抗性攻击的脆弱性持怀疑态度。尽管已经提出了各种防御方法，医学深度学习方法的应用仍然存在担忧。这是由于与各种高质量的自然图像数据集相比，医学成像的一些弱点，例如缺乏足够数量的高质量图像和标记数据。本文回顾了最近提出的针对医学成像 DNN 的对抗性攻击方法以及针对这些攻击的防御技术。它还讨论了这些方法的不同方面，并为提高神经网络的鲁棒性提供了未来的方向。本文回顾了最近提出的针对医学成像 DNN 的对抗性攻击方法以及针对这些攻击的防御技术。它还讨论了这些方法的不同方面，并为提高神经网络的鲁棒性提供了未来的方向。本文回顾了最近提出的针对医学成像 DNN 的对抗性攻击方法以及针对这些攻击的防御技术。它还讨论了这些方法的不同方面，并为提高神经网络的鲁棒性提供了未来的方向。