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.

翻译：无监督异常检测因其能够减轻昂贵的像素级标注负担而在医学影像领域受到广泛关注。为实现这一目标，现代方法通常利用生成模型生成病变图像的健康参考，再通过比较健康参考与原始病变图像来识别异常区域。近年来，扩散模型凭借其良好的模式覆盖性和高样本质量，在医学图像无监督异常检测中展现出巨大潜力。然而，医学图像固有的低对比度特性及人体复杂解剖结构使得重建任务极具挑战性，同时医学图像的全局信息往往未能得到充分利用。针对这两个问题，我们提出了一种新颖的掩码自编码器增强扩散模型（MAEDiff）用于脑图像无监督异常检测。该模型采用分层块划分策略，通过重叠上层块生成健康图像，并基于掩码自编码器在下层块上实现机制，以增强对未噪声区域的约束条件。在肿瘤和多发性硬化病变数据上的大量实验证明了该方法的有效性。