文献类型：期刊文献

英文题名：Unsupervised Brain Anomaly Detection Using Structure-Preserving Noise Generation and Multi-Scale Dual-Expert Ensembles

作者：Yang, Qianyi[1,2];Huang, Bingcang[3];Zhou, Qin[4];Wang, Zhe[4];Chen, Kai[3];Tang, Xiu[2,5];Yao, Chang[2,5];Wu, Sai[2,5]

机构：[1]Zhejiang Univ, Hangzhou 310058, Peoples R China;[2]Zhejiang Univ, Inst Blockchain & Data Secur, Hangzhou High Tech Zone Binjiang, Hangzhou 310058, Peoples R China;[3]Gongli Hosp Shanghai Pudong New Area, Dept Radiol, Shanghai 200135, Peoples R China;[4]East China Univ Sci & Technol, Dept Comp Sci & Engn, Shanghai 200237, Peoples R China;[5]Zhejiang Univ, State Key Lab Blockchain & Data Secur, Hangzhou 310058, Peoples R China

年份：2026

卷号：30

期号：6

起止页码：4849

外文期刊名：IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS

收录：;WOS:【SCI-EXPANDED(收录号:WOS:001785997900013)】；

基金：This work was supported in part by the National Natural Science Foundation of China under Grant 62201341, in part by the Key Research and Development Project of Yunnan Province under Grant 202402AD080006, and in part by the Youth Innovation Project of the Ningbo "Yongjiang Talent Program" under Grant2024A-156-G.

语种：英文

外文关键词：Noise; Image reconstruction; Anomaly detection; Autoencoders; Noise reduction; Training; Brain modeling; Magnetic resonance imaging; Ensemble learning; Standards; Cross-modal cutmix; dual-expert ensemble learning; structure-preserving noise generation; unsupervised anomaly detection

摘要：Detecting early brain anomalies is crucial for patient prognosis and recovery, but obtaining expert-annotated data is challenging, especially for clinically silent early brain anomalies. Unsupervised brain anomaly detection, which identifies anomalous regions by modeling normal brain patterns, has gained interest for its label efficiency. However, the inherent variability in normal brains and subtle anomalies that closely resemble normal tissue pose challenges for traditional autoencoders in distinguishing anomalies. Denoising AutoEncoder (DAE) methods have been explored to enhance the model's ability, while their success hinges on effective noise generation strategies. In this paper, we introduce a novel, structure-preserving noise generation scheme based on cross-modal CutMix, aiming to enhance the diversity of noise patterns while preserving the anatomical structure of the brain. To enhance the robustness of DAE learning, we propose an ensemble approach featuring dual experts, each incorporating distinct scale of noise. This dual-expert scheme effectively amplifies reconstruction errors in anomalous regions and suppresses false alarms in healthy areas. Additionally, we propose an anatomically-aware bidirectional consistency loss to ensure high-fidelity reconstruction at the regional level, using superpixels for anatomy perception and bidirectional distillation for reliable knowledge transfer. Extensive experiments across two different settings demonstrate the effectiveness and generalization ability of our proposed method.