论文标题
捍卫对医学成像AI系统，分类或检测的对抗性攻击？
Defending against adversarial attacks on medical imaging AI system, classification or detection?
论文作者
论文摘要
医学成像AI系统（例如疾病分类和细分）越来越多地从基于计算机视觉的AI系统中启发和转变。尽管已经开发并证明了一系列基于基于损失功能的对抗训练和/或基于损失功能的防御技术在计算机视觉中有效，但防御对医学图像的对抗性攻击仍然很大程度上仍然是一个未知的领域，这是由于以下独特的挑战：1）在医疗图像中的标记稀缺性差异很大程度上限制了AI系统的可逆性性； 2）医学图像中非常相似和主导的前后和背景使得很难学习不同疾病类别之间的区分特征； 3）在整个医学形象中加上精心的对抗噪声，而不是集中的器官目标可以使干净和对抗性的例子比不同的疾病类别更具歧视性。在本文中，我们提出了一种基于半监督的对抗训练（SSAT）和无监督的对抗检测（UAD）的新型强大医学成像AI框架，然后设计了一种新的措施来评估系统对抗风险。我们系统地证明了使用基准OCT成像数据集的对抗性攻击的不同现实世界中，在不同的对抗性攻击环境下，强大的医学成像AI系统的优势比现有的对抗防御技术的优势。
Medical imaging AI systems such as disease classification and segmentation are increasingly inspired and transformed from computer vision based AI systems. Although an array of adversarial training and/or loss function based defense techniques have been developed and proved to be effective in computer vision, defending against adversarial attacks on medical images remains largely an uncharted territory due to the following unique challenges: 1) label scarcity in medical images significantly limits adversarial generalizability of the AI system; 2) vastly similar and dominant fore- and background in medical images make it hard samples for learning the discriminating features between different disease classes; and 3) crafted adversarial noises added to the entire medical image as opposed to the focused organ target can make clean and adversarial examples more discriminate than that between different disease classes. In this paper, we propose a novel robust medical imaging AI framework based on Semi-Supervised Adversarial Training (SSAT) and Unsupervised Adversarial Detection (UAD), followed by designing a new measure for assessing systems adversarial risk. We systematically demonstrate the advantages of our robust medical imaging AI system over the existing adversarial defense techniques under diverse real-world settings of adversarial attacks using a benchmark OCT imaging data set.
