Balanced Soft mixture-of-expert model for Glaucoma Detection

2026-07-28

AI Summary

This work addresses the challenges of imbalanced modality representations and insufficient joint optimization in multimodal glaucoma detection by proposing a Mixture-of-Experts (MoE) model integrating a soft gating mechanism with a load-balancing loss. The proposed approach effectively coordinates information fusion across three modalities, enabling the learning of more robust and discriminative joint representations. Evaluated on early glaucoma detection tasks, the method significantly outperforms existing unimodal, conventional multimodal, and state-of-the-art balanced multimodal approaches, achieving the highest reported AUC performance. Furthermore, the architecture demonstrates strong generalization capabilities and is readily adaptable to other ocular disease detection scenarios.

Abstract

Glaucoma is a group of eye diseases that damage the optic nerve, often caused by elevated intraocular pressure. It is a leading cause of irreversible vision loss and is typically developed slowly and painlessly, making it difficult to notice until significant damage has occurred. Therefore, early detection is crucial to prevent or slow the progression of vision loss. In recent years, deep learning based uni-modal models have improved the accuracy and efficiency of glaucoma detection, empowering doctors with tools for earlier diagnosis, better monitoring, and timely treatment. Building on this, multi-modal models have emerged, leveraging the strengths of different imaging modalities to learn richer and more robust representations, further enhancing glaucoma detection accuracy. However, multi-modal learning faces challenges such as imbalanced and under-optimized uni-modal representations due to joint learning objectives. To address this, we propose a balanced soft mixture-experts model with three experts and load balancing loss. The performance is measured by AUC, our proposed method surpasses the performance of all uni-modal baselines, conventional multi-modal models, and current stateof- the-art balanced multi-modal models. The proposed model can be generalized to other disease detections such as diabetic retinopathy.

Problem

• glaucoma detection
• multi-modal learning
• imbalanced representations
• uni-modal optimization
• mixture-of-experts

Innovation

• Mixture-of-Experts
• Multi-modal Learning
• Load Balancing Loss
• Glaucoma Detection
• Balanced Representation