This chapter 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.

翻译：本章探讨了利用去噪扩散模型进行医学图像异常定位的方法。在简要介绍此类模型的方法学背景（包括其在图像重建中的应用及基于引导机制的条件化处理）后，我们概述了适用于医学图像异常定位任务的现有数据集与评估指标。在此框架下，我们讨论了从全监督分割到半监督、弱监督、自监督及无监督方法的监督范式谱系，并深入分析了各类方法的有效性与局限性。此外，我们着重指出了异常定位领域面临的开放挑战，包括检测偏差、域偏移、计算成本与模型可解释性等问题。本文旨在系统综述该领域当前的研究进展，指明现存的研究空白，并展望扩散模型在实现鲁棒医学图像异常定位方面的潜力。