用多模型融合提升眼底病分类的鲁棒性与效率
Many-MobileNet: Multi-Model Augmentation for Robust Retinal Disease Classification
- 训练多个不同复杂度模型，搭配多样数据增强策略
- 在数据稀缺场景下实现更强泛化能力，兼顾效率与特征提取
- 适合资源受限但需高可靠性的医疗图像分类任务
本文提出 Many-MobileNet，一种基于轻量级 CNN 架构的眼底疾病分类模型融合策略。针对过拟合和数据集多样性不足的问题，通过训练多个采用不同数据增强方式和模型复杂度的子模型，并进行融合，显著提升了在数据稀缺场景下的泛化性能。该方法在保持计算高效的同时增强了特征表达能力，适用于实际医疗影像分析中的可靠性需求。
原文摘要 · Abstract (English)
In this work, we propose Many-MobileNet, an efficient model fusion strategy for retinal disease classification using lightweight CNN architecture. Our method addresses key challenges such as overfitting and limited dataset variability by training multiple models with distinct data augmentation strategies and different model complexities. Through this fusion technique, we achieved robust generalization in data-scarce domains while balancing computational efficiency with feature extraction capabilities.
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