AnoDODE: Anomaly Detection with Diffusion ODE

TL;DR

This paper introduces AnoDODE, a novel anomaly detection method using diffusion ODEs to estimate feature density in medical images, enabling effective anomaly identification and localization with interpretability.

Contribution

The paper proposes a new diffusion ODE-based anomaly detection approach that estimates feature density for improved detection and localization in medical imaging.

Findings

Outperforms existing anomaly detection methods on BraTS2021 dataset

Provides both anomaly detection and interpretability at image and pixel levels

Demonstrates robustness and effectiveness in clinical medical image analysis

Abstract

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.