Journal article
A self-adaptive deep learning method for automated eye laterality detection based on color fundus photography
C Liu, X Han, Z Li, J Ha, G Peng, W Meng, M He
Plos One | PUBLIC LIBRARY SCIENCE | Published : 2019
Open access
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. Resu..
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Awarded by University of Melbourne
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.