Self-Supervised Anomaly Segmentation via Diffusion Models with Dynamic Transformer UNet

Komal Kumar, Snehashis Chakraborty, Dwarikanath Mahapatra, Behzad Bozorgtabar, Sudipta Roy; Proceedings of the Winter Conference on Applications of Computer Vision (WACV), 2025, pp. 7917-7927

A robust anomaly detection mechanism should possess the capability to effectively remediate anomalies restoring them to a healthy state while preserving essential healthy information. Despite the efficacy of existing generative models in learning the underlying distribution of healthy reference data they face primary challenges when it comes to efficiently repair larger anomalies or anomalies situated near high pixel-density regions. In this paper we introduce a self-supervised anomaly detection method based on a diffusion model that samples from multi-frequency four dimensional simplex noise and makes predictions using our proposed Dynamic Transformer UNet (DTUNet). This simplex-based noise function helps address primary problems to some extent and is scalable for three-dimensional and colored images. In the evolution of ViT our developed architecture serving as the backbone for the diffusion model is tailored to treat time and noise image patches as tokens. We incorporate long skip connections bridging the shallow and deep layers along with smaller skip connections within these layers. Furthermore we integrate a partial diffusion Markov process which reduces sampling time thus enhancing scalability. Our method surpasses existing generative-based anomaly detection methods across three diverse datasets which include BrainMRI Brats2021 and the MVtec dataset. It achieves an average improvement of +10.1% in Dice coefficient +10.4% in IOU and +9.6% in AUC. Our source code is made publicly available on Github.