The introduction of diffusion models in anomaly detection has paved the way for more effective and accurate image reconstruction in pathologies. However, the current limitations in controlling noise granularity hinder diffusion models' ability to generalize across diverse anomaly types and compromise the restoration of healthy tissues. To overcome these challenges, we propose AutoDDPM, a novel approach that enhances the robustness of diffusion models. AutoDDPM utilizes diffusion models to generate initial likelihood maps of potential anomalies and seamlessly integrates them with the original image. Through joint noised distribution re-sampling, AutoDDPM achieves harmonization and in-painting effects. Our study demonstrates the efficacy of AutoDDPM in replacing anomalous regions while preserving healthy tissues, considerably surpassing diffusion models' limitations. It also contributes valuable insights and analysis on the limitations of current diffusion models, promoting robust and interpretable anomaly detection in medical imaging - an essential aspect of building autonomous clinical decision systems with higher interpretability.

翻译：扩散模型在异常检测中的引入为病理图像重建开辟了更高效、精准的路径。然而，当前对噪声粒度的控制局限阻碍了扩散模型在多样化异常类型上的泛化能力，并影响健康组织的正常恢复。针对上述挑战，我们提出AutoDDPM——一种增强扩散模型鲁棒性的创新方法。AutoDDPM利用扩散模型生成潜在异常的初始似然图，并将其与原始图像无缝融合。通过联合噪声分布重采样，该方法实现图像协调与修复效果。研究表明，AutoDDPM在替换异常区域的同时能有效保留健康组织，显著突破现有扩散模型的技术瓶颈。此外，本研究揭示了当前扩散模型的局限性并提供了深入分析，推动了医学影像中可解释、鲁棒的异常检测发展——这一方向对构建具有更高可解释性的自主临床决策系统具有关键意义。