用组织掩码指导细胞分类，减少标注依赖，提升病理图像分析精度
Tissue Aware Nuclei Detection and Classification Model for Histopathology Images
- 基于卷积神经网络与组织掩码联合建模，实现点级监督下的细胞检测与分类
- 在PUMA数据集上超越现有方法，对上皮、内皮等组织特异性细胞识别显著提升
- 首次将组织上下文信息融入单细胞分类，适合医学图像标注资源有限的场景
准确的细胞核检测与分类是计算病理学的基础，但现有方法依赖详尽专家标注且未充分利用组织上下文。我们提出TAND框架，通过组织掩码条件化点级监督，实现细胞核的联合检测与分类。该模型结合基于ConvNeXt的编码器-解码器与冻结的Virchow-2组织分割分支，利用多尺度空间特征线性调制（Spatial-FiLM）机制，使语义组织概率选择性调节分类分支。在PUMA基准测试中，TAND达到领先性能，显著优于无组织感知基线及掩码监督方法。尤其在上皮、内皮和间质等组织依赖型细胞类型上表现突出。据我们所知，这是首个将学习到的组织掩码用于单细胞分类的方法，为降低标注负担提供了可行路径。
原文摘要 · Abstract (English)
Accurate nuclei detection and classification are fundamental to computational pathology, yet existing approaches are hindered by reliance on detailed expert annotations and insufficient use of tissue context. We present Tissue-Aware Nuclei Detection (TAND), a novel framework achieving joint nuclei detection and classification using point-level supervision enhanced by tissue mask conditioning. TAND couples a ConvNeXt-based encoder-decoder with a frozen Virchow-2 tissue segmentation branch, where semantic tissue probabilities selectively modulate the classification stream through a novel multi-scale Spatial Feature-wise Linear Modulation (Spatial-FiLM). On the PUMA benchmark, TAND achieves state-of-the-art performance, surpassing both tissue-agnostic baselines and mask-supervised methods. Notably, our approach demonstrates remarkable improvements in tissue-dependent cell types such as epithelium, endothelium, and stroma. To the best of our knowledge, this is the first method to condition per-cell classification on learned tissue masks, offering a practical pathway to reduce annotation burden.
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