用于宫颈细胞学图像分类的几何感知高斯先验和轴向注意力

中文摘要 AI 辅助

准确的宫颈细胞学图像分类是自动宫颈癌筛查的关键组成部分，从巴氏涂片图像中可靠识别正常、癌前和癌症相关细胞模式可提高筛查效率和诊断一致性。但该任务具挑战性，因宫颈细胞形态复杂、类内差异细微、类间相似性强。现有基于卷积的模型能很好捕捉局部纹理，但建模长程关系能力有限；基于注意力的模型能提供更广泛上下文，但常缺乏明确结构指导。为解决这些局限，我们提出一个面向宫颈癌筛查的细胞学图像分析的几何感知分类框架，融合从预训练视觉语言特征中学到的语义抽象和结构先验。该方法用高斯专家模块从全局语义信息生成轴向先验，捕捉核排列和细胞空间组织等结构规律。这些先验嵌入轴向自注意力模块，调节沿水平和垂直方向的相似性计算，改善长程依赖建模和结构敏感特征交互。在Mendeley液基细胞学和SIPaKMeD数据集上的实验表明，该方法在前一数据集上准确率达99.48%，后一数据集上达96.08%，在召回率、精确率和整体分类性能上均有均衡提升。视觉分析进一步表明，学到的先验突出了诊断相关细胞区域，证明了该框架作为宫颈细胞学筛查导向决策支持工具的潜力。

英文摘要

Accurate cervical cytology image classification is a key component of automated cervical cancer screening, where reliable recognition of normal, precancerous, and cancer-associated cellular patterns from Pap smear images can improve screening efficiency and diagnostic consistency. However, this task remains challenging because cervical cells exhibit complex morphology, subtle intra-class variations, and strong inter-class similarities. Existing convolution-based models capture local texture well but have limited ability to model long-range relationships, whereas attention-based models provide broader context but often lack explicit structural guidance. To address these limitations, we propose a geometry-aware classification framework for cervical cancer screening-oriented cytology image analysis, incorporating semantic abstraction and structural priors learned from pre-trained vision-language features. The method uses Gaussian expert modules to generate axis-wise priors from global semantic information, capturing structural regularities such as nuclear alignment and cellular spatial organization. These priors are embedded into an axial self-attention module to modulate similarity computation along horizontal and vertical directions, improving long-range dependency modeling and structure-sensitive feature interaction. Experiments on the Mendeley liquid-based cytology and SIPaKMeD datasets show that the proposed method achieves 99.48% accuracy on the former and 96.08% on the latter, with balanced gains in recall, precision, and overall classification performance. Visual analysis further shows that the learned priors highlight diagnostically relevant cellular regions, demonstrating the potential of the proposed framework as a screening-oriented decision-support tool for cervical cytology.

发表机构

• University of Science and Technology of China（中国科学技术大学）

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