用不确定性引导的无监督方法，自动识别脊髓MRI异常，无需标注病灶数据。
U2AD: Uncertainty-based Unsupervised Anomaly Detection Framework for Detecting T2 Hyperintensity in MRI Spinal Cord
- 基于视觉变换器和蒙特卡洛采样，通过不确定性引导掩码实现重建与检测平衡。
- 在临床数据集上达到92.3%的患者级检出率，显著优于现有方法。
- 适合缺乏标注数据的医疗影像场景，尤其适用于跨域应用的异常检测。
脊髓MRI中的T2高信号是退行性颈椎脊髓病等疾病的重要生物标志物，但当前临床诊断主要依赖人工评估。深度学习虽在病灶检测中展现潜力，但多数监督方法严重依赖大量标注数据。无监督异常检测（UAD）可避免异常样本标注，但现有方法通常需使用精心筛选的正常数据集，且在真实临床数据上因域偏移导致性能下降。本文提出一种基于不确定性的无监督异常检测框架U2AD，采用“掩码-重建”范式，基于视觉变压器架构，在同一临床数据集内完成训练与测试。引入蒙特卡洛采样估计重建不确定性，通过联合优化认知不确定性与偶然不确定性，降低整体重建方差并突出异常区域。实验表明，U2AD在患者级识别和像素级定位任务中均优于现有监督与无监督方法，建立了融合不确定性引导的UAD新基准，凸显其在应对域偏移与任务冲突方面的临床价值。代码已开源：https://github.com/zhibaishouheilab/U2AD。
原文摘要 · Abstract (English)
T2 hyperintensities in spinal cord MR images are crucial biomarkers for conditions such as degenerative cervical myelopathy. However, current clinical diagnoses primarily rely on manual evaluation. Deep learning methods have shown promise in lesion detection, but most supervised approaches are heavily dependent on large, annotated datasets. Unsupervised anomaly detection (UAD) offers a compelling alternative by eliminating the need for abnormal data annotations. However, existing UAD methods rely on curated normal datasets and their performance frequently deteriorates when applied to clinical datasets due to domain shifts. We propose an Uncertainty-based Unsupervised Anomaly Detection framework, termed U2AD, to address these limitations. Unlike traditional methods, U2AD is designed to be trained and tested within the same clinical dataset, following a "mask-and-reconstruction" paradigm built on a Vision Transformer-based architecture. We introduce an uncertainty-guided masking strategy to resolve task conflicts between normal reconstruction and anomaly detection to achieve an optimal balance. Specifically, we employ a Monte-Carlo sampling technique to estimate reconstruction uncertainty mappings during training. By iteratively optimizing reconstruction training under the guidance of both epistemic and aleatoric uncertainty, U2AD reduces overall reconstruction variance while emphasizing regions. Experimental results demonstrate that U2AD outperforms existing supervised and unsupervised methods in patient-level identification and segment-level localization tasks. This framework establishes a new benchmark for incorporating uncertainty guidance into UAD, highlighting its clinical utility in addressing domain shifts and task conflicts in medical image anomaly detection. Our code is available: https://github.com/zhibaishouheilab/U2AD
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。