CFCH：用于眼前节疾病分析的粗细协作分层学习
CFCH: Coarse-Fine Collaborative Hierarchical Learning for Anterior Segment Disease Analysis
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中文总结 AI 辅助
针对眼前节疾病分类忽视解剖-疾病层级结构的问题，提出CFCH双分支粗细协作分层学习框架，并构建大规模数据集AS-9K，在两个数据集上超越现有方法。
中文摘要 AI 辅助
眼前节疾病的准确分类对于眼科筛查和诊断至关重要。然而，由于成像条件的显著差异以及眼部病理固有的解剖-疾病层级结构，裂隙灯图像分析仍然具有挑战性。现有方法通常将此任务视为平面多分类问题，忽略了解剖区域（如角膜、结膜和晶状体）与疾病之间的结构化依赖关系。为解决这些局限性，我们提出了CFCH，一种粗细协作分层学习框架，通过双分支架构显式建模解剖上下文和疾病语义。为了实现有效的跨粒度协作，CFCH引入了语义和跨粒度注意力一致性约束，鼓励各分支学习对齐且互补的特征。此外，我们构建了AS-9K，一个包含8975张图像、覆盖12种常见疾病类别的大规模眼前节数据集。据我们所知，AS-9K是最大的公开可用的眼前节图像分类数据集。在两个眼前节数据集上的大量实验表明，CFCH优于最先进的方法。定性可视化进一步显示了更聚焦且与病变相关的激活响应，验证了所提出框架的有效性。代码将在此https URL提供。
英文摘要
Accurate classification of anterior segment diseases is crucial for ophthalmic screening and diagnosis. However, slit-lamp image analysis remains challenging due to substantial variability in imaging conditions and the intrinsic anatomical-disease hierarchy of ocular pathologies. Existing methods typically formulate this task as a flat multi-class classification problem, ignoring the structured dependency between anatomical regions (e.g., cornea, conjunctiva, and lens) and disease manifestations.To address these limitations, we propose CFCH, a Coarse-Fine Collaborative Hierarchical learning framework that explicitly models anatomical context and disease semantics through a dual-branch architecture. To enable effective cross-granularity collaboration, CFCH introduces semantic and cross-granularity attention consistency constraints, encouraging aligned yet complementary feature learning across branches. In addition, we construct AS-9K, a large-scale anterior segment dataset with 8975 images covering 12 common disease categories. To the best of our knowledge, AS-9K is the largest publicly available dataset for anterior segment image classification. Extensive experiments on two anterior segment datasets demonstrate that CFCH outperforms state-of-the-art methods. Qualitative visualizations further show more focused and lesion-relevant activation responses, validating the effectiveness of the proposed framework. Code will be available at https://github.com/ybupengwang/CFCH.
