Diffusion Models with Implicit Guidance for Medical Anomaly Detection

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 reverse diffusion process by integrating implicit guidance through intermediate masks. THOR aims to preserve the integrity of healthy tissue details in reconstructed images, ensuring fidelity to the original scan in areas unaffected by pathology. Comparative evaluations reveal that THOR surpasses existing diffusion-based methods in retaining detail and precision in image restoration and detecting and segmenting anomalies in brain MRIs and wrist X-rays. Code: https://github.com/compai-lab/2024-miccai-bercea-thor.git .