针对宫颈涂片检测难题，提出中心感知的高效检测框架。
Center-Aware Detection with Swin-based Co-DETR Framework for Cervical Cytology
- 将检测转为中心点预测，适配固定尺寸标注数据
- 通过几何优化与增强策略提升定位精度，显著降低误检
- 在挑战赛中获1项第1名、1项第2名，适合医学图像分析场景
自动化宫颈涂片分析对宫颈癌筛查至关重要，但密集细胞分布与复杂形态仍带来挑战。本文提出参赛方案，在RIVA宫颈涂片挑战赛中取得Track A第2名、Track B第1名。基于Swin-Large骨干网络与Co-DINO框架，构建强大基线以实现多尺度特征提取。针对数据集固定的边界框标注，将检测任务建模为中心点预测问题，引入中心保持型数据增强与解析几何框优化方法，有效抑制定位抖动。最后通过任务特异性损失调优，适配不同赛道需求。实验表明，针对性优化显著提升检测性能，形成可复现的细胞图像分析流程。代码已开源：https://github.com/YanKong0408/Center-DETR。
原文摘要 · Abstract (English)
Automated analysis of Pap smear images is critical for cervical cancer screening but remains challenging due to dense cell distribution and complex morphology. In this paper, we present our winning solution for the RIVA Cervical Cytology Challenge, achieving 1st place in Track B and 2nd place in Track A. Our approach leverages a powerful baseline, integrating the Co-DINO framework with a Swin-Large backbone for robust multi-scale feature extraction. To address the dataset's unique fixed-size bounding box annotations, we formulate the detection task as a center-point prediction problem. Tailoring our approach to this formulation, we introduce a center-preserving data augmentation strategy and an analytical geometric box optimization to effectively absorb localization jitter. Finally, we apply track-specific loss tuning to adapt the loss weights for each task. Experiments demonstrate that our targeted optimizations improve detection performance, providing an effective pipeline for cytology image analysis. Our code is available at https://github.com/YanKong0408/Center-DETR.
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