Diffusion Models with Implicit Guidance for Medical Anomaly Detection
Cosmin I. Bercea Benedikt Wiestler Daniel Rueckert Julia A. Schnabel
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.