针对复杂环境下眼白异常分割难题，提出分级双专家网络模型
HD-DinoMoE: A Class-Aware Hierarchical Dual Mixture-of-Experts Network for Scleral Anomaly Segmentation in Complex Acquisition Scenarios
- 设计类感知双流融合与多专家解码架构，提升细粒度分割能力
- 在混合场景数据集上实现72.11%的平均Dice，有效抑制反光区误检
- 适用于中医眼科辅助诊断系统，适合真实临床与用户拍摄场景
中医眼科望诊为眼白表面异常提供经验线索，但临床应用主观性强、难量化。为支持智能量化诊断，本文构建了中医启发的人工智能眼科辅助诊断系统（TAO），聚焦眼白像素级异常分割。针对临床及用户拍摄图像中存在的多源分布差异、多样异常形态及眼白反光（SSR）问题，提出类感知分层双专家网络HD-DinoMoE。该模型结合类感知双流DINOv3特征融合与类特定多专家解码，实现对血管、黄斑点、黑斑点三类异常的分割。采用三阶段冻结主干路由策略稳定双主干适配；引入渐进置信度惩罚（PCP）损失降低反光区域高置信度误检与分割泄漏；设计类感知自适应采样加权（CA-ASW）平衡样本与类别层面训练贡献。进一步构建多标签眼白异常分割数据集（ML-SASD），包含临床、野生与混合三种设置，含三类异常的像素级标注。在ML-SASD-Mix上，HD-DinoMoE取得72.11%平均Dice与58.44%平均交并比，同时保持良好边界定位与反光区误检控制，并在公开SBVPI数据集血管子集上展现良好泛化能力。结果表明，该方法为复杂采集条件下TAO提供了可行的分割解决方案。代码与数据见https://github.com/FX-CMX/HD-DinoMoE。
原文摘要 · Abstract (English)
Traditional Chinese Medicine (TCM) ocular inspection provides empirical cues for assessing scleral surface anomalies, but its clinical use remains subjective and difficult to quantify. To support intelligent and quantifiable ocular inspection, this study presents the TCM-inspired Artificial Intelligence Ocular Auxiliary Diagnosis System (TAO) and focuses on pixel-level scleral surface anomaly segmentation. For clinical and user-acquired images affected by multi-source distributional discrepancies, diverse anomaly morphologies, and scleral specular reflection (SSR), we propose HD-DinoMoE, a class-aware hierarchical dual mixture-of-experts network. HD-DinoMoE combines class-aware dual-stream DINOv3 feature fusion with class-specific multi-expert decoding to segment Vessels, Yellow and Black Spots, and Blood Spots. A three-stage backbone-frozen routing strategy stabilizes dual-backbone adaptation; Progressive Confidence Penalty (PCP) Loss reduces high-confidence false positives and segmentation leakage in SSR regions; and Class-Aware Adaptive Sample Weighting (CA-ASW) balances sample- and class-level training contributions. We further construct the Multi-label Scleral Anomaly Segmentation Dataset (ML-SASD), a new benchmark with Clinical, Wild, and Mix settings and pixel-wise annotations for three anomaly categories. On ML-SASD-Mix, HD-DinoMoE achieves a mean Dice of 72.11% and a mean Intersection-over-Union of 58.44%, while maintaining favorable boundary localization and specular-region false-positive control. It also shows competitive generalization on the Vessels subset of the public SBVPI dataset. These results indicate that HD-DinoMoE provides a feasible segmentation solution for TAO under complex acquisition scenarios. The code and data access information are available at https://github.com/FX-CMX/HD-DinoMoE.
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