用扩散模型定位医学图像异常，兼顾精度与可解释性。
Denoising Diffusion Models for Anomaly Localization in Medical Images
- 基于扩散模型的去噪机制实现异常区域精确定位
- 涵盖从全监督到无监督的多种标注策略，适应不同临床场景
- 揭示检测偏差、域偏移等关键挑战，指导未来研究
本文综述了利用去噪扩散模型进行医学图像异常定位的研究进展。在简要介绍扩散模型的方法学背景及其在图像重建中的应用和引导机制后，系统梳理了适用于该任务的数据集与评估指标。讨论了从完全监督分割到半监督、弱监督、自监督及无监督等多种监督范式，分析其有效性与局限性。此外，重点指出当前面临的开放挑战，包括检测偏差、域偏移、计算成本高及模型可解释性差等问题。旨在总结领域最新成果，识别研究空白，并强调扩散模型在实现鲁棒医学图像异常定位方面的潜力。
原文摘要 · Abstract (English)
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.
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