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This paper introduces a balanced soft mixture-of-expert model designed to enhance glaucoma detection by addressing the limitations of existing multi-modal approaches. By employing a load balancing loss and integrating three expert networks, the model effectively mitigates issues related to imbalanced uni-modal representations, leading to superior performance. The results demonstrate that this method outperforms all baseline models, including conventional and state-of-the-art multi-modal frameworks, highlighting its potential for broader applications in disease detection.
A novel balanced soft mixture-of-expert model achieves unprecedented accuracy in glaucoma detection, outpacing existing multi-modal and uni-modal approaches.
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.
