EPDiff: Erasure Perception Diffusion Model for Unsupervised Anomaly Detection in Preoperative Multimodal Images.

Unsupervised anomaly detection (UAD) methods typically detect anomalies by learning and reconstructing the normative distribution. However, since anomalies constantly invade and affect their surroundings, sub-healthy areas in the junction present structural deformations that could be easily misidentified as anomalies, posing difficulties for UAD methods that solely learn the normative distribution. The use of multimodal images can facilitate to address the above challenges, as they can provide complementary information of anomalies. Therefore, this paper propose a novel method for UAD in preoperative multimodal images, called Erasure Perception Diffusion model (EPDiff). First, the Local Erasure Progressive Training (LEPT) framework is designed to better rebuild sub-healthy structures around anomalies through the diffusion model with a two-phase process. Initially, healthy images are used to capture deviation features labeled as potential anomalies. Then, these anomalies are locally erased in multimodal images to progressively learn sub-healthy structures, obtaining a more detailed reconstruction around anomalies. Second, the Global Structural Perception (GSP) module is developed in the diffusion model to realize global structural representation and correlation within images and between modalities through interactions of high-level semantic information. In addition, a training-free module, named Multimodal Attention Fusion (MAF) module, is presented for weighted fusion of anomaly maps between different modalities and obtaining binary anomaly outputs. Experimental results show that EPDiff improves the AUPRC and mDice scores by 2% and 3.9% on BraTS2021, and by 5.2% and 4.5% on Shifts over the state-of-the-art methods, which proves the applicability of EPDiff in diverse anomaly diagnosis. The code is available at https://github.com/wjiazheng/EPDiff.