Diffusion Models with Implicit Guidance for Medical Anomaly Detection

• URL: http://arxiv.org/abs/2403.08464v1
• Date: Wed, 13 Mar 2024 12:26:55 GMT
• Title: Diffusion Models with Implicit Guidance for Medical Anomaly Detection
• Authors: Cosmin I. Bercea and Benedikt Wiestler and Daniel Rueckert and Julia A. Schnabel
• Abstract summary: Temporal Harmonization for Optimal Restoration (THOR) aims to preserve the integrity of healthy tissue in areas unaffected by pathology. Relative evaluations show THOR surpasses existing diffusion-based methods in detecting and segmenting anomalies in brain MRIs and wrist X-rays.
• Score: 13.161402789616004
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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 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.

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