MedIAnomaly: A comparative study of anomaly detection in medical images
Yu Cai Weiwen Zhang Hao Chen Kwang-Ting Cheng
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
\url{https://github.com/caiyu6666/MedIAnomaly}.