CFCH: Coarse-Fine Collaborative Hierarchical Learning for Anterior Segment Disease Analysis

Peng Wang; Haohan Zou; Yanlin Wu; Xueshuo Xie; Yan Wang; Tao Li

cs.CVSep 26, 2026

CFCH: Coarse-Fine Collaborative Hierarchical Learning for Anterior Segment Disease Analysis

Authors: Peng Wang, Haohan Zou, Yanlin Wu, Xueshuo Xie, Yan Wang, Tao Li

Organizations: College of Cryptology and Cyber Science Nankai University, Tianjin, China · College of Engineering Yanbian University, Yanji, China · College of Computer Science Nankai University, Tianjin, China · Haihe Lab of ITAI Tianjin, China ·

Abstract

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.

Figures & tables

Panel of Figure 1 — Fig. 1: Example slit-lamp images of anterior segment disease categories, showing diverse pathological manifestations across anatomical regions.

Hierarchical Classification

Hierarchical classification uses relationships among labels to predict categories at multiple levels, from broad groups to fine-grained classes.

See all 89 papers

Fundus Image-based Glaucoma Screening via Retinal Knowledge-Oriented Dynamic Multi-Level Feature Integration
Apr 14, 2026Chi Liu, Yuzhuo Zhou, Sheng Shen +9Glaucoma Detection Medical Image Analysis
Faculty of Data Science, City University of Macau, Avenida Padre Tomás Pereira, Taipa, 999078, Macao, China · State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Vision Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou, 510000, Guangdong, China · Hainan International College, Minzu University of China, Li’an International Education Innovation Pilot Zone, Lingshui, 572423, Hainan, China +4
HD-DinoMoE: A Class-Aware Hierarchical Dual Mixture-of-Experts Network for Scleral Anomaly Segmentation in Complex Acquisition Scenarios
Jun 3, 2026Yinxiang Yu, Maoxiang Chu, Qi Niu +7Medical Imaging Ophthalmic Image Analysis
School of Electronic and Information Engineering, University of Science and Technology Liaoning, No.189 Qianshan Middle Road, Lishan District, Anshan, 114051, Liaoning, China
PC-Seg: Progressive Cross-View Consistency for 3D OCT Segmentation from Sparse 2D Annotations
Jul 20, 2026Tsubasa Konno, Takahiro Ninomiya, Yukun Zhou +6Multi-View Consistency Optical Coherence Tomography
Graduate School of Information Sciences, Tohoku University, Japan · Department of Ophthalmology, Graduate School of Medicine, Tohoku University · Institute of Ophthalmology, University College London, UK +3

Apr 14, 2026J/K move · Enter open · S save

Fundus Image-based Glaucoma Screening via Retinal Knowledge-Oriented Dynamic Multi-Level Feature Integration

Chi Liu, Yuzhuo Zhou, Sheng Shen, Zongyuan Ge+8

Faculty of Data Science, City University of Macau, Avenida Padre Tomás Pereira, Taipa, 999078, Macao, China · State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Vision Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou, 510000, Guangdong, China · Hainan International College, Minzu University of China, Li’an International Education Innovation Pilot Zone, Lingshui, 572423, Hainan, China+4

Glaucoma Detection Medical Image Analysis Medical Diagnosis Retinal Disease Classification Fundus Imaging

Open paper AskPDF arXiv

While deep learning has advanced automated glaucoma screening via color fundus photography, existing purely data-driven models often overfit to confounding imaging artifacts and struggle to capture unpredictable pathological cues located beyond predefined anatomical boundaries. To address these limitations, we propose a retinal knowledge-oriented framework that synergizes dynamic multi-scale feature learning with domain-specific anatomical priors. The proposed architecture adopts a tri-branch structure to jointly model the global retinal context, the structural characteristics of the optic cup and disc, and dynamically cropped pathological regions. Specifically, we devise a Dynamic Window Mechanism (DWM) that adaptively discovers diagnostically informative patches via image-level supervision. Furthermore, we introduce a Knowledge-Enhanced Convolutional Block Attention Module (KE-CBAM) that explicitly incorporates retinal priors from a pre-trained foundation model RETFound to guide spatial attention, preventing the network from assigning spurious weights to irrelevant background noise. Extensive evaluations on the large-scale AIROGS dataset demonstrate that our method achieves a state-of-the-art AUC of

98.5\%

and an accuracy of

94.6\%

. More importantly, the integration of anatomical priors effectively mitigates the inherent class imbalance, significantly improving the detection of referable glaucoma cases. Additional validations on the SMDG-19 benchmark further confirm its superior cross-domain generalization, indicating that our contribution provides a robust, interpretable, and scalable solution for real-world clinical glaucoma diagnosis. Our code is available at https://github.com/magiczhuo/Glaucoma-Detection

Abstract

Hierarchical Classification

Explore similar work

Fundus Image-based Glaucoma Screening via Retinal Knowledge-Oriented Dynamic Multi-Level Feature Integration

HD-DinoMoE: A Class-Aware Hierarchical Dual Mixture-of-Experts Network for Scleral Anomaly Segmentation in Complex Acquisition Scenarios

PC-Seg: Progressive Cross-View Consistency for 3D OCT Segmentation from Sparse 2D Annotations