It can be challenging to identify brain MRI anomalies using supervised deep-learning techniques due to anatomical heterogeneity and the requirement for pixel-level labeling. Unsupervised anomaly detection approaches provide an alternative solution by relying only on sample-level labels of healthy brains to generate a desired representation to identify abnormalities at the pixel level. Although, generative models are crucial for generating such anatomically consistent representations of healthy brains, accurately generating the intricate anatomy of the human brain remains a challenge. In this study, we present a method called masked-DDPM (mDPPM), which introduces masking-based regularization to reframe the generation task of diffusion models. Specifically, we introduce Masked Image Modeling (MIM) and Masked Frequency Modeling (MFM) in our self-supervised approach that enables models to learn visual representations from unlabeled data. To the best of our knowledge, this is the first attempt to apply MFM in DPPM models for medical applications. We evaluate our approach on datasets containing tumors and numerous sclerosis lesions and exhibit the superior performance of our unsupervised method as compared to the existing fully/weakly supervised baselines. Code is available at https://github.com/hasan1292/mDDPM.

翻译：由于解剖异质性及需要像素级标注，使用监督深度学习方法识别脑部MRI异常具有挑战性。无监督异常检测方法提供了一种替代方案，仅依赖健康大脑的样本级标签生成目标表示，从而在像素级识别异常。尽管生成模型对于生成解剖结构一致的健康大脑表征至关重要，但精确生成人脑复杂的解剖结构仍是一个难题。本研究提出一种名为masked-DDPM (mDPPM) 的方法，该方法引入基于掩码的正则化来重构扩散模型的生成任务。具体而言，我们在自监督方法中引入掩码图像建模 (MIM) 和掩码频率建模 (MFM)，使模型能够从无标注数据中学习视觉表征。据我们所知，这是首次尝试将MFM应用于医疗领域的DPPM模型。我们在包含肿瘤和多发性硬化症病灶的数据集上评估了该方法，结果表明我们的无监督方法相较于现有全监督/弱监督基线方法具有更优性能。代码可在 https://github.com/hasan1292/mDDPM 获取。