C-Norm：细胞分布归一化实现医学细胞图像的精确识别
C-Norm: Cell-Distribution Normalization Enables Precision Recognition of Medical-Cell Image
- Chongqing University Cancer Hospital（重庆大学附属肿瘤医院）
- Chongqing University of Posts and Telecommunications（重庆邮电大学）
- Shanghai First Maternity and Infant Hospital, Tongji University（同济大学附属第一妇婴保健院）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
针对TCT图像人工阅片问题及现有AI检测模型局限，提出C-Norm方法，通过解耦重组合并细胞、集成YOLOv12与DINOv3模块，实现细胞群体均匀分布，提升TCT图像识别性能，优于主流算法。
AI中文摘要：
薄层液基细胞学检测（TCT）可进行早期宫颈癌筛查，但人工阅片耗时且细胞病理学家的诊断结果不一致。现有AI检测模型在实际临床条件下表现不佳，主要受两个关键限制：TCT载玻片上细胞群体的空间分布不平衡，以及依赖专业病理学家标注的高质量细胞学数据有限。为解决这些限制，我们提出了一种细胞分布归一化（C-Norm）方法。通过将异常和正常细胞从原始TCT图像中解耦并重新合成，该方法确保细胞群体的均匀分布，从而减轻分布偏差导致的泛化退化。在此基础上，我们将YOLOv12框架与DINOv3模块集成。这种混合架构利用YOLO模型的先进检测能力和DINOv3的卓越特征表示来捕捉TCT图像精确识别所需的细微形态差异。大量实验表明，我们提出的方法实现了最先进的性能，显著优于主流检测算法。完整实现可在：此https URL获取。
英文摘要：
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
