Journal article

A self-adaptive deep learning method for automated eye laterality detection based on color fundus photography

Chi Liu, Xiaotong Han, Zhixi Li, Jason Ha, Guankai Peng, Wei Meng, Mingguang He

PLOS ONE | PUBLIC LIBRARY SCIENCE | Published : 2019

Abstract

PURPOSE: To provide a self-adaptive deep learning (DL) method to automatically detect the eye laterality based on fundus images. METHODS: A total of 18394 fundus images with real-world eye laterality labels were used for model development and internal validation. A separate dataset of 2000 fundus images with eye laterality labeled manually was used for external validation. A DL model was developed based on a fine-tuned Inception-V3 network with self-adaptive strategy. The area under receiver operator characteristic curve (AUC) with sensitivity and specificity and confusion matrix were applied to assess the model performance. The class activation map (CAM) was used for model visualization. RE..

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University of Melbourne Researchers

Grants

Awarded by National Key R&D Program of China


Awarded by Fundamental Research Funds of the State Key Laboratory in Ophthalmology, Science and Technology Planning Project of Guangdong Province


Funding Acknowledgements

This work was supported by the National KeyR&D Program of China (2018YFC0116500). MH receives support from the Fundamental Research Funds of the State Key Laboratory in Ophthalmology, Science and Technology Planning Project of Guangdong Province 2013B20400003. MH receives support from the University of Melbourne at Research Accelerator Program and the CERA Foundation. The Center for Eye Research Australia receives Operational Infrastructure Support from the Victorian State Government. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.