提出细胞分布归一化方法，提升宫颈癌筛查图像识别精度
C-Norm: Cell-Distribution Normalization Enables Precision Recognition of Medical-Cell Image
- 分离并重合成正常与异常细胞，均衡图像中细胞分布
- 在TCT数据集上达到94.3%检测准确率，优于主流模型
- 适合医疗影像分析、尤其是细胞病理图像识别研究者
薄层液基细胞学检查（TCT）可用于宫颈癌早期筛查，但人工阅片耗时且诊断结果不一致。现有AI模型在真实临床条件下表现不佳，主要受限于两个关键问题：TCT切片中细胞群体的空间分布不平衡，以及依赖专业病理科医师标注的高质量数据稀缺。为此，本文提出细胞分布归一化（C-Norm）方法，通过将原始TCT图像中的异常与正常细胞解耦并重新合成，实现细胞分布的均匀化，从而缓解因分布偏移导致的泛化性能下降。在此基础上，集成YOLOv12框架与DINOv3模块，结合YOLO的高效检测能力与DINOv3的优越特征表示能力，捕捉细微形态差异以实现精确识别。大量实验表明，该方法在多个指标上达到当前最优表现，显著超越主流检测算法。完整代码已公开于：https://github.com/ddw2AIGROUP2CQUPT/Cell-Norm
原文摘要 · Abstract (English)
ThinPrep Cytologic Test (TCT) enables early cervical cancer screening, but manual reading is time-consuming and yields inconsistent diagnostic results among cytopathologists. Existing AI detection models perform poorly under real clinical conditions, primarily restricted by two key constraints: unbalanced spatial distribution of cell populations in TCT slides, and limited high-quality annotated cytology data relying on professional pathologist labeling. To address these limitations, we propose a Cell-Distribution Normalization (C-Norm) method. By decoupling abnormal and normal cells from the original TCT images and re-synthesizing them, this method ensures a uniform distribution of cell populations, thereby mitigating generalization degradation caused by distribution bias. Building upon this, we integrate the YOLOv12 framework with a DINOv3 module. This hybrid architecture leverages the advanced detection capability of YOLO models and the superior feature representations of DINOv3 to capture subtle morphological nuances essential for precise recognition of TCT images. Extensive experiments demonstrate that our proposed method achieves state-of-the-art performance, significantly outperforming mainstream detection algorithms. The complete implementation is available at: https://github.com/ddw2AIGROUP2CQUPT/Cell-Norm
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