  Unsupervised anomaly detection has gained significant attention in the field
of medical imaging due to its capability of relieving the costly pixel-level
annotation. To achieve this, modern approaches usually utilize generative
models to produce healthy references of the diseased images and then identify
the abnormalities by comparing the healthy references and the original diseased
images. Recently, diffusion models have exhibited promising potential for
unsupervised anomaly detection in medical images for their good mode coverage
and high sample quality. However, the intrinsic characteristics of the medical
images, e.g. the low contrast, and the intricate anatomical structure of the
human body make the reconstruction challenging. Besides, the global information
of medical images often remain underutilized. To address these two issues, we
propose a novel Masked Autoencoder-enhanced Diffusion Model (MAEDiff) for
unsupervised anomaly detection in brain images. The MAEDiff involves a
hierarchical patch partition. It generates healthy images by overlapping
upper-level patches and implements a mechanism based on the masked autoencoders
operating on the sub-level patches to enhance the condition on the unnoised
regions. Extensive experiments on data of tumors and multiple sclerosis lesions
demonstrate the effectiveness of our method.
