الملخص
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.
الكلمات المفتاحية
الموضوع
بيانات النشر
- المجلة
- غير متاح
- وصول مفتوح
- وصول مفتوح أخضر
اقتبس هذه المقالة
APA 7
Wang, P., Zou, H., Wu, Y., Xie, X., Wang, Y., & Li, T. (2026). CFCH: Coarse-Fine Collaborative Hierarchical Learning for Anterior Segment Disease Analysis. https://omanscience.com/ar/articles/cfch-coarse-fine-collaborative-hierarchical-learning-for-anterior-segment-disease-analysis
MLA 9
Wang, Peng, et al. "CFCH: Coarse-Fine Collaborative Hierarchical Learning for Anterior Segment Disease Analysis." https://omanscience.com/ar/articles/cfch-coarse-fine-collaborative-hierarchical-learning-for-anterior-segment-disease-analysis.
شيكاغو (المؤلف–التاريخ)
Wang, Peng, Haohan Zou, Yanlin Wu, Xueshuo Xie, Yan Wang, and Tao Li. 2026. "CFCH: Coarse-Fine Collaborative Hierarchical Learning for Anterior Segment Disease Analysis." https://omanscience.com/ar/articles/cfch-coarse-fine-collaborative-hierarchical-learning-for-anterior-segment-disease-analysis.
هارفارد
Wang, P., Zou, H., Wu, Y., Xie, X., Wang, Y. and Li, T. (2026) 'CFCH: Coarse-Fine Collaborative Hierarchical Learning for Anterior Segment Disease Analysis', Available at: https://omanscience.com/ar/articles/cfch-coarse-fine-collaborative-hierarchical-learning-for-anterior-segment-disease-analysis.
فانكوفر
Wang P, Zou H, Wu Y, Xie X, Wang Y, Li T. CFCH: Coarse-Fine Collaborative Hierarchical Learning for Anterior Segment Disease Analysis. https://omanscience.com/ar/articles/cfch-coarse-fine-collaborative-hierarchical-learning-for-anterior-segment-disease-analysis
IEEE
P. Wang, H. Zou, Y. Wu, X. Xie, Y. Wang, and T. Li, "CFCH: Coarse-Fine Collaborative Hierarchical Learning for Anterior Segment Disease Analysis," https://omanscience.com/ar/articles/cfch-coarse-fine-collaborative-hierarchical-learning-for-anterior-segment-disease-analysis.