Possibilities of Explainable Artificial Intelligence for Glaucoma Detection Using the LIME Method as an Example
Author:
Affiliation:
1. Dubna State University,Dubna,Russia
2. Federal Research Center “Informatics and Management” of the Russian Academy of Sciences,Moscow,Russia
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/10158985/10159032/10159038.pdf?arnumber=10159038
Reference37 articles.
1. An interpretable ensemble deep learning model for diabetic retinopathy disease classification;et al;2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC),2019
2. 1450 fundus images with 899 glaucoma data and 551 normal data;song;IEEE Dataport,2020
3. Graphlime: Local interpretable model explanations for graph neural networks;et al;IEEE Transactions on Knowledge and Data Engineering,2022
4. Towards multi-center glaucoma OCT image screening with semi-supervised joint structure and function multi-task learning;et al;Medical Image Analysis,2020
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1. Hybrid Explainable Framework for Diabetic Retinopathy Classification from Fundus Images;2024 XXVII International Conference on Soft Computing and Measurements (SCM);2024-05-22
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