发表机构
University of Louisiana at Lafayette; School of Computing and Informatics（路易斯安那大学拉斐特分校; 计算与信息学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对青光眼检测，提出含三个专家及负载平衡损失的平衡软专家混合模型，解决多模态学习问题，其性能通过AUC衡量，超越多种基线和模型，还可推广至其他疾病检测。
AI 中文摘要
青光眼是一组损害视神经的眼部疾病，常由眼压升高引起，是不可逆视力丧失的主要原因，早期难以察觉。近年来，基于深度学习的单模态模型提高了青光眼检测的准确性和效率，在此基础上多模态模型应运而生。然而，多模态学习面临单模态表示不平衡和优化不足等挑战。为此，我们提出了一个具有三个专家和负载平衡损失的平衡软专家混合模型。通过AUC衡量性能，该方法超越了所有单模态基线、传统多模态模型和当前最先进的平衡多模态模型，且可推广到其他疾病检测。
英文摘要
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.
Comments18 pages, 5 figures
