用超图计算融合细胞空间关联与深度判别特征，提升宫颈癌细胞检测精度。
High-Precision Mixed Feature Fusion Network Using Hypergraph Computation for Cervical Abnormal Cell Detection
- 基于超图计算的跨层级特征融合策略，建模细胞间空间关联。
- 在三个公开数据集上检测准确率显著优于现有方法。
- 适合医学图像分析、智能辅助诊断系统研发人员参考。
从薄层液基细胞学检查（TCT）图像中自动检测异常宫颈细胞，是构建智能计算机辅助诊断系统的关键环节。然而，现有算法通常无法有效建模视觉特征间的关联性，而这些空间相关特征实际上包含关键诊断信息。此外，尚无检测算法能同时整合细胞间的互相关特征与细胞内部的判别性特征，缺乏端到端检测模型的融合策略。本文提出一种基于超图计算的细胞检测网络，有效融合不同类型特征，结合空间相关特征与深层判别特征。具体地，采用多层级融合子网络（MLF-SNet）增强特征提取能力；引入基于超图计算的跨层级特征融合策略（CLFFS-HC）模块，实现混合特征集成。最后，在三个公开可用数据集上进行了实验，结果表明该方法显著提升了宫颈异常细胞检测性能。
原文摘要 · Abstract (English)
Automatic detection of abnormal cervical cells from Thinprep Cytologic Test (TCT) images is a critical component in the development of intelligent computer-aided diagnostic systems. However, existing algorithms typically fail to effectively model the correlations of visual features, while these spatial correlation features actually contain critical diagnostic information. Furthermore, no detection algorithm has the ability to integrate inter-correlation features of cells with intra-discriminative features of cells, lacking a fusion strategy for the end-to-end detection model. In this work, we propose a hypergraph-based cell detection network that effectively fuses different types of features, combining spatial correlation features and deep discriminative features. Specifically, we use a Multi-level Fusion Sub-network (MLF-SNet) to enhance feature extractioncapabilities. Then we introduce a Cross-level Feature Fusion Strategy with Hypergraph Computation module (CLFFS-HC), to integrate mixed features. Finally, we conducted experiments on three publicly available datasets, and the results demonstrate that our method significantly improves the performance of cervical abnormal cell detection.
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