In medical imaging, segmentation models have known a significant improvement in the past decade and are now used daily in clinical practice. However, similar to classification models, segmentation models are affected by adversarial attacks. In a safety-critical field like healthcare, certifying model predictions is of the utmost importance. Randomized smoothing has been introduced lately and provides a framework to certify models and obtain theoretical guarantees. In this paper, we present for the first time a certified segmentation baseline for medical imaging based on randomized smoothing and diffusion models. Our results show that leveraging the power of denoising diffusion probabilistic models helps us overcome the limits of randomized smoothing. We conduct extensive experiments on five public datasets of chest X-rays, skin lesions, and colonoscopies, and empirically show that we are able to maintain high certified Dice scores even for highly perturbed images. Our work represents the first attempt to certify medical image segmentation models, and we aspire for it to set a foundation for future benchmarks in this crucial and largely uncharted area.

翻译：在医学影像领域，分割模型在过去十年中取得了显著进步，现已广泛应用于临床实践。然而，与分类模型类似，分割模型同样面临对抗性攻击的影响。在医疗保健这类安全关键型领域，验证模型预测结果至关重要。近期提出的随机平滑技术为模型认证提供了理论保障框架。本文首次基于随机平滑与扩散模型，提出了针对医学影像的认证分割基线方案。实验结果表明，利用去噪扩散概率模型的优势有助于突破随机平滑的局限性。我们在五个公开数据集（涵盖胸部X光片、皮肤病变和结肠镜影像）上开展广泛实验，实证证明即使在高度扰动图像中，仍能维持高认证Dice分数。本研究首次尝试认证医学图像分割模型，期望为该重要且尚待深入探索的领域奠定未来基准基础。