针对伤口和量尺的精准分割，提出双注意力网络与贝叶斯调参方法。
Dual-Attention U-Net++ with Class-Specific Ensembles and Bayesian Hyperparameter Optimization for Precise Wound and Scale Marker Segmentation
- 融合通道与空间注意力机制，缓解医学图像类不平衡问题。
- 在竞赛数据集上取得0.8640的加权F1分数，优于传统方法。
- 适合需要高精度分割的临床伤口评估场景。
临床图像中伤口和量尺的精确分割仍是重大挑战，对有效伤口管理和自动化评估至关重要。本文提出一种新型双注意力U-Net++架构，结合通道注意力（SCSE）与空间注意力机制，有效应对医学图像中的严重类别不平衡和多样性问题。通过五折交叉验证，在多种架构与编码器中筛选出EfficientNet-B7为最优编码器。随后，分别训练两个针对不同类别的模型，采用定制化预处理、大量数据增强及贝叶斯超参数优化（WandB搜寻）。最终模型集成采用测试时增强提升预测可靠性。方法在NBC 2025 & PCBBE 2025竞赛基准数据集上评估，性能以加权F1分数（75%伤口，25%量尺）衡量，由竞赛组织方在未公开硬件上计算，结果达0.8640，证明其在复杂医学分割任务中的有效性。
原文摘要 · Abstract (English)
Accurate segmentation of wounds and scale markers in clinical images remainsa significant challenge, crucial for effective wound management and automatedassessment. In this study, we propose a novel dual-attention U-Net++ archi-tecture, integrating channel-wise (SCSE) and spatial attention mechanisms toaddress severe class imbalance and variability in medical images effectively.Initially, extensive benchmarking across diverse architectures and encoders via 5-fold cross-validation identified EfficientNet-B7 as the optimal encoder backbone.Subsequently, we independently trained two class-specific models with tailoredpreprocessing, extensive data augmentation, and Bayesian hyperparameter tun-ing (WandB sweeps). The final model ensemble utilized Test Time Augmentationto further enhance prediction reliability. Our approach was evaluated on a bench-mark dataset from the NBC 2025 & PCBBE 2025 competition. Segmentationperformance was quantified using a weighted F1-score (75% wounds, 25% scalemarkers), calculated externally by competition organizers on undisclosed hard-ware. The proposed approach achieved an F1-score of 0.8640, underscoring itseffectiveness for complex medical segmentation tasks.
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