CG-HAF：一种用于智能护肤支持中序数痤疮严重程度分级可解释的全局-局部病灶负担融合框架
CG-HAF: An Interpretable Global-Local Lesion-Burden Fusion Framework for Ordinal Acne Severity Grading in Agentic Skincare Support
- International Islamic University Chittagong (IIUC)（吉大港国际伊斯兰大学）
- Rajshahi University of Engineering and Technology (RUET)（拉杰沙希工程技术大学）
- King Saud University（沙特国王大学）
- Charles Sturt University（查尔斯特大学）
- The University of Queensland（昆士兰大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
CG-HAF提出可解释的全局-局部融合框架，结合整体概率与病灶描述符进行序数痤疮分级，在基准上显著提升，并揭示跨数据集需标准对齐。
AI中文摘要：
序数痤疮严重程度分级需要区分视觉上相似的相邻等级，同时综合权衡整体面部外观和局部病灶负担——这一证据在大多数现有方法中被压缩为单一的不透明表示。我们提出CG-HAF，一种全局-局部融合框架，该框架保持证据显式化：将来自独立训练分类器的平均整体严重程度概率，与来自目标检测器的结构化病灶负担描述符（病灶计数、检测置信度、病灶面积）相结合，形成紧凑表示，再由轻量级可解释分类器产生最终等级。在广泛使用的基准上，这种融合相对于仅使用全局证据的基线产生了清晰且统计上支持的改进，在最严重的病例上提升最大。在具有不同分级标准的独立数据集上的测试表明，强组内性能不会自动迁移，后续诊断将这一差距大部分归因于分级标准不匹配，而非仅检测失败。这些发现支持可解释的全局-局部融合作为序数痤疮分级的一种有效策略，同时强调标准对齐是跨数据集可移植性的关键，并进一步展示了所得严重程度信号如何支持护肤应用中透明、非诊断性的决策。
英文摘要：
Ordinal acne severity grading requires distinguishing visually similar neighboring grades while jointly weighing holistic facial appearance and localized lesion burden - evidence that most existing approaches collapse into a single opaque representation. We introduce CG-HAF, a global-local fusion framework that instead keeps this evidence explicit: averaged holistic severity probabilities from independently trained classifiers are combined with structured lesion-burden descriptors from an object detector (lesion count, detection confidence, lesion area) into a compact representation, from which a lightweight, interpretable classifier produces the final grade. On a widely used benchmark, this fusion yields a clear, statistically supported improvement over global-evidence-only baselines, with the largest gains on the most severe cases. Testing on an independent dataset with a different grading standard shows that strong within-dataset performance does not transfer automatically, and a follow-up diagnostic attributes much of this gap to mismatched grading criteria rather than detection failure alone. These findings support interpretable global-local fusion as an effective strategy for ordinal acne grading while highlighting criterion alignment as key to cross-dataset portability, with a further illustration of how the resulting severity signal can support transparent, non-diagnostic decision-making in skincare applications.
