Dimensions overall score 7.0

• The proposed approach achieved 1st place in Track B and 2nd place in Track A of the RIVA Cervical Cytology Challenge.result 95%
• The method integrates the Co-DINO framework with a Swin-Large backbone for robust multi-scale feature extraction.technical 90%
• The detection task is formulated as a center-point prediction problem to address fixed-size bounding box annotations.method 90%
• A center-preserving data augmentation strategy was introduced to tailor the approach to center-point prediction.method 85%
• An analytical geometric box optimization was developed to effectively absorb localization jitter.technical 85%
• Track-specific loss tuning was applied to adapt loss weights for each task.method 85%
• The targeted optimizations improve detection performance for cytology image analysis.result 80%
• The approach provides an effective pipeline for automated analysis of Pap smear images.technical 75%

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