用热图引导的查询机制提升星形胶质细胞检测鲁棒性
Heatmap Guided Query Transformers for Robust Astrocyte Detection across Immunostains and Resolutions
- 热图引导生成空间锚点，精准定位微弱细小细胞
- 轻量级Transformer提升密集区域判别力，减少误检
- 适用于多种染色和分辨率，适合病理图像分析
星形胶质细胞是神经疾病的重要标志物，其复杂分支结构与染色依赖性变异使得组织学图像中自动检测极具挑战。为此，我们提出一种混合CNN-Transformer检测器，结合局部特征提取与全局上下文推理。热图引导的查询机制为微小且模糊的星形胶质细胞生成空间锚点，轻量级Transformer模块增强密集簇中的区分能力。在ALDH1L1和GFAP染色数据集上评估，模型优于Faster R-CNN、YOLOv11和DETR，FROC分析显示更高敏感度且假阳性更少。结果表明该混合架构在鲁棒星形胶质细胞检测中具有潜力，可为计算病理学工具提供基础。
原文摘要 · Abstract (English)
Astrocytes are critical glial cells whose altered morphology and density are hallmarks of many neurological disorders. However, their intricate branching and stain dependent variability make automated detection of histological images a highly challenging task. To address these challenges, we propose a hybrid CNN Transformer detector that combines local feature extraction with global contextual reasoning. A heatmap guided query mechanism generates spatially grounded anchors for small and faint astrocytes, while a lightweight Transformer module improves discrimination in dense clusters. Evaluated on ALDH1L1 and GFAP stained astrocyte datasets, the model consistently outperformed Faster R-CNN, YOLOv11 and DETR, achieving higher sensitivity with fewer false positives, as confirmed by FROC analysis. These results highlight the potential of hybrid CNN Transformer architectures for robust astrocyte detection and provide a foundation for advanced computational pathology tools.
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