Anomaly detection is the process of identifying atypical data samples that significantly deviate from the majority of the dataset. In the realm of clinical screening and diagnosis, detecting abnormalities in medical images holds great importance. Typically, clinical practice provides access to a vast collection of normal images, while abnormal images are relatively scarce. We hypothesize that abnormal images and their associated features tend to manifest in low-density regions of the data distribution. Following this assumption, we turn to diffusion ODEs for unsupervised anomaly detection, given their tractability and superior performance in density estimation tasks. More precisely, we propose a new anomaly detection method based on diffusion ODEs by estimating the density of features extracted from multi-scale medical images. Our anomaly scoring mechanism depends on computing the negative log-likelihood of features extracted from medical images at different scales, quantified in bits per dimension. Furthermore, we propose a reconstruction-based anomaly localization suitable for our method. Our proposed method not only identifie anomalies but also provides interpretability at both the image and pixel levels. Through experiments on the BraTS2021 medical dataset, our proposed method outperforms existing methods. These results confirm the effectiveness and robustness of our method.

翻译：异常检测是指识别显著偏离数据集大多数样本的异常数据样本的过程。在临床筛查与诊断领域，检测医学图像中的异常具有至关重要的意义。通常情况下，临床实践中可获取大量正常图像，而异常图像相对稀缺。我们假设异常图像及其相关特征倾向于出现在数据分布的低密度区域。基于这一假设，我们利用扩散ODE（扩散常微分方程）进行无监督异常检测，因其在密度估计任务中具有可处理性和优越性能。具体而言，我们提出一种基于扩散ODE的新型异常检测方法，通过估计多尺度医学图像提取特征的密度实现检测。我们的异常评分机制依赖于计算不同尺度下医学图像提取特征的负对数似然（以每维比特数量化）。此外，我们提出一种适用于本方法的基于重建的异常定位策略。所提方法不仅能识别异常，还能在图像级和像素级提供可解释性。通过在BraTS2021医学数据集上的实验，我们的方法优于现有方法。这些结果证实了我们方法的有效性和鲁棒性。