AnoDODE: Anomaly Detection with Diffusion ODE
Xianyao Hu Congming Jin
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.