通过局部补丁排序实现细粒度医学图像异常检测
Unsupervised Patch-GAN with Targeted Patch Ranking for Fine-Grained Novelty Detection in Medical Imaging
- 基于未标注数据的补丁级生成对抗网络，学习正常纹理特征
- 在ISIC和BraTS数据集上分别达到95.79%和96.05%的AUC
- 适合处理小范围、隐蔽性异常的医学影像分析场景
由于罕见病灶标注数据稀缺，且其表现形式多样、细节细微，医学影像中新型异常检测面临挑战。当微小异常区域嵌入大范围正常组织时，整体图像预测常忽略这些细微偏差。为此，我们提出一种无监督补丁生成对抗网络框架，通过重建掩码图像来学习细粒度正常特征，增强对正常态微小偏离的敏感性。将重建图像划分为补丁，评估每个补丁的真实性，实现更精细的异常定位，克服整体图像评估的局限。此外，补丁排序机制优先关注高异常得分区域，强化局部差异与全局上下文的一致性。在ISIC 2016皮肤病变和BraTS 2019脑肿瘤数据集上的实验表明，该框架分别获得95.79%和96.05%的AUC，优于三种先进基线方法。
原文摘要 · Abstract (English)
Detecting novel anomalies in medical imaging is challenging due to the limited availability of labeled data for rare abnormalities, which often display high variability and subtlety. This challenge is further compounded when small abnormal regions are embedded within larger normal areas, as whole-image predictions frequently overlook these subtle deviations. To address these issues, we propose an unsupervised Patch-GAN framework designed to detect and localize anomalies by capturing both local detail and global structure. Our framework first reconstructs masked images to learn fine-grained, normal-specific features, allowing for enhanced sensitivity to minor deviations from normality. By dividing these reconstructed images into patches and assessing the authenticity of each patch, our approach identifies anomalies at a more granular level, overcoming the limitations of whole-image evaluation. Additionally, a patch-ranking mechanism prioritizes regions with higher abnormal scores, reinforcing the alignment between local patch discrepancies and the global image context. Experimental results on the ISIC 2016 skin lesion and BraTS 2019 brain tumor datasets validate our framework's effectiveness, achieving AUCs of 95.79% and 96.05%, respectively, and outperforming three state-of-the-art baselines.
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