Many-MobileNet: Multi-model Augmentation for Robust Retinal Disease Classification

Hao Wang, Wenhui Zhu, Xuanzhao Dong, Yanxi Chen, Xin Li, Peijie Qiu, Xiwen Chen, Vamsi Krishna Vasa, Yujian Xiong, Oana M. Dumitrascu, Abolfazl Razi, Yalin Wang

Abstract: 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 over-fitting 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 environments while balancing computational efficiency with feature extraction capabilities. As a result, we secured 3rd place in the MICCAI UWF4DR 2024 Challenge for image quality assessment in ultra-widefield fundus images. Our software package is available at https://github.com/Retinal-Research/NN-MOBILENET.

External IDs:dblp:conf/miccai/WangZDCLQCVXDRW24