March 14, 2024, 4:42 a.m. | Cosmin I. Bercea, Benedikt Wiestler, Daniel Rueckert, Julia A. Schnabel

cs.LG updates on

arXiv:2403.08464v1 Announce Type: cross
Abstract: 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 anomaly detection arxiv cs.lg detection diffusion diffusion models eess.iv guidance medical type

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