Center-Aware Detection with Swin-based Co-DETR Framework for Cervical Cytology
This addresses cervical cancer screening via Pap smear analysis, though it is incremental as it builds on existing Co-DINO and Swin-Large frameworks with task-specific optimizations.
The paper tackled automated detection in cervical cytology images by formulating detection as a center-point prediction problem, achieving 1st place in Track B and 2nd place in Track A of the RIVA Cervical Cytology Challenge.
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.