Diffusion models have advanced unsupervised anomaly detection by improving the transformation of pathological images into pseudo-healthy equivalents. Nonetheless, standard approaches may compromise critical information during pathology removal, leading to restorations that do not align with unaffected regions in the original scans. Such discrepancies can inadvertently increase false positive rates and reduce specificity, complicating radiological evaluations. This paper introduces Temporal Harmonization for Optimal Restoration (THOR), which refines the de-noising process by integrating implicit guidance through temporal anomaly maps. THOR aims to preserve the integrity of healthy tissue in areas unaffected by pathology. Comparative evaluations show that THOR surpasses existing diffusion-based methods in detecting and segmenting anomalies in brain MRIs and wrist X-rays. Code: https://github.com/ci-ber/THOR_DDPM.

翻译：扩散模型通过将病理图像转化为伪健康等效图像，推动了无监督异常检测的发展。然而，标准方法在去除病理过程中可能损害关键信息，导致复原结果与原始扫描中未受影响区域不一致。这种不一致会无意中增加假阳性率并降低特异性，使放射学评估复杂化。本文提出了时间协调最优复原（THOR）方法，该方法通过整合基于时间异常图的隐式引导来优化去噪过程。THOR旨在保持未受病理影响区域中健康组织的完整性。对比评估表明，THOR在脑部MRI和腕部X光片异常检测与分割方面优于现有基于扩散的方法。代码：https://github.com/ci-ber/THOR_DDPM。