Anomaly detection (AD) aims at detecting abnormal samples that deviate from the expected normal patterns. Generally, it can be trained merely on normal data, without a requirement for abnormal samples, and thereby plays an important role in rare disease recognition and health screening in the medical domain. Despite the emergence of numerous methods for medical AD, the lack of a fair and comprehensive evaluation causes ambiguous conclusions and hinders the development of this field. To address this problem, this paper builds a benchmark with unified comparison. Seven medical datasets with five image modalities, including chest X-rays, brain MRIs, retinal fundus images, dermatoscopic images, and histopathology images, are curated for extensive evaluation. Thirty typical AD methods, including reconstruction and self-supervised learning-based methods, are involved in comparison of image-level anomaly classification and pixel-level anomaly segmentation. Furthermore, for the first time, we systematically investigate the effect of key components in existing methods, revealing unresolved challenges and potential future directions. The datasets and code are available at https://github.com/caiyu6666/MedIAnomaly.

翻译：异常检测（Anomaly Detection, AD）旨在检测偏离预期正常模式的异常样本。通常，该方法仅需在正常数据上进行训练，无需异常样本，因此在医学领域的罕见疾病识别与健康筛查中扮演着重要角色。尽管医学异常检测领域已涌现出众多方法，但由于缺乏公平且全面的评估，导致结论模糊不清，阻碍了该领域的发展。为解决此问题，本文构建了一个统一对比的基准。我们收集了包含五种图像模态的七个医学数据集用于广泛评估，包括胸部X光片、脑部磁共振成像、视网膜眼底图像、皮肤镜图像和组织病理学图像。研究涵盖了三十种典型的异常检测方法，包括基于重建和自监督学习的方法，并进行了图像级异常分类与像素级异常分割的比较。此外，我们首次系统性地研究了现有方法中关键组件的影响，揭示了尚未解决的挑战与潜在的未来方向。数据集与代码公开于 https://github.com/caiyu6666/MedIAnomaly。