Anomaly detection (AD) aims at detecting abnormal samples that deviate from
the expected normal patterns. Generally, it can be trained on merely normal
data without the requirement for abnormal samples, and thereby plays an
important role in the recognition of rare diseases and health screening in the
medical domain. Despite numerous related studies, we observe a lack of a fair
and comprehensive evaluation, which causes some ambiguous conclusions and
hinders the development of this field. This paper focuses on building a
benchmark with unified implementation and comparison to address this problem.
In particular, seven medical datasets with five image modalities, including
chest X-rays, brain MRIs, retinal fundus images, dermatoscopic images, and
histopathology whole slide images are organized for extensive evaluation.
Twenty-seven 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, we for the
first time formally explore the effect of key components in existing methods,
clearly revealing unresolved challenges and potential future directions. The
datasets and code are available at
https://github.com/caiyu6666/MedIAnomaly.

该研究论文旨在建立一个统一实施和比较的基准，对医学领域的七个包括胸部 X 射线、脑部磁共振、视网膜底像、皮肤镜像和组织病理全切片图像在内的医学数据集进行广泛评估，并比较了二十七种典型的异常检测方法，首次对现有方法中的关键组件进行了正式探索，明确了未解决的挑战和潜在的未来方向。