Enhancing Diffusion Models Towards Anomaly-Aware Reconstructions for Medical Image Anomaly Detection

Abstract

Diffusion-based unsupervised anomaly detection in medical images has emerged as an effective paradigm, leveraging unlabelled healthy data to precisely characterize the distribution of normal anatomy and identify a wide range of pathological abnormalities. The method reconstructs a pseudo-healthy image from a potentially anomalous input and identifies anomalies by measuring pixel-wise reconstruction errors. However, existing approaches often preserve anomalous regions in the reconstruction, resulting in less prominent anomaly segmentation. Additionally, their inability to accurately restore normal areas can lead to increased false positives. In this work, we propose CS-Unet to advance this paradigm by realizing the concept of anomaly-aware reconstruction, defined as reconstructions that are consciously devoid of anomalies while faithfully restoring normal regions. Firstly, we propose a compression-expansion DenseNet (CompExDenseNet), which performs a dense cascade of nonlinear dimension transformations to extract compact feature representations, suppressing the reconstruction of anomalous patterns. Secondly, we design an attention gate (AG) unit to control the flow of low-frequency information, mitigating the leakage of anomalous information. Finally, we propose a frequency-domain adaptive residual convolution (FreAR) module that selectively enhances the most relevant frequency components to facilitate high-fidelity restoration of normal regions. Experimental results demonstrate that CS-Unet achieves outstanding performance in unsupervised anomaly detection, confirming its effectiveness.