Denoising Diffusion Models for Anomaly Localization in Medical Images

2024/10/31 by Cosmin I. Bercea, Bercea, Cosmin I., Philippe C. Cattin +5 · 1 voice · 2 citations
Computer Science · #Anomaly Detection Techniques and Applications #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #FOS: Electrical engineering #Image and Video Processing (eess.IV) #electronic engineering #information engineering

paper · pdf · doi:10.48550/arxiv.2410.23834

openalex publication_date 2024/10/31 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

This review explores anomaly localization in medical images using denoising diffusion models. After providing a brief methodological background of these models, including their application to image reconstruction and their conditioning using guidance mechanisms, we provide an overview of available datasets and evaluation metrics suitable for their application to anomaly localization in medical images. In this context, we discuss supervision schemes ranging from fully supervised segmentation to semi-supervised, weakly supervised, self-supervised, and unsupervised methods, and provide insights into the effectiveness and limitations of these approaches. Furthermore, we highlight open challenges in anomaly localization, including detection bias, domain shift, computational cost, and model interpretability. Our goal is to provide an overview of the current state of the art in the field, outline research gaps, and highlight the potential of diffusion models for robust anomaly localization in medical images.

Cited by

• Group Equivariant Diffusion for Anomaly Detection in Computational Cytology
• MIAS-SAM: Medical Image Anomaly Segmentation without thresholding

Discussions

• 🎯 Authors survey denoising diffusion models for anomaly localization in medical imaging, discussing datasets, supervision levels, and key challenges, and highlighting the models’ potential for robust [bsky, 0 points, 0 comments]

Related

• Diffusion Models for Medical Anomaly Detection
• Statistical Test for Diffusion-Based Anomaly Localization via Selective Inference
• AnoDODE: Anomaly Detection with Diffusion ODE
• Mask, Stitch, and Re-Sample: Enhancing Robustness and Generalizability in Anomaly Detection through Automatic Diffusion Models
• Synomaly Noise and Multi-Stage Diffusion: A Novel Approach for Unsupervised Anomaly Detection in Medical Images