Attention-based deep learning framework for automatic fundus image processing to aid in diabetic retinopathy grading-Reference-Cited by-同舟云学术

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Attention-based deep learning framework for automatic fundus image processing to aid in diabetic retinopathy grading

Published:2024-06 Issue: Volume:249 Page:108160
ISSN:0169-2607
Container-title:Computer Methods and Programs in Biomedicine
language:en
Short-container-title:Computer Methods and Programs in Biomedicine

Author:

Romero-Oraá Roberto^ORCID,Herrero-Tudela María^ORCID,López María I.,Hornero Roberto^ORCID,García María^ORCID

Funder

Centro de Investigación Biomédica en Red Bioingeniería Biomateriales y Nanomedicina

Universidad de Valladolid

European Regional Development Fund

Gobierno de España Ministerio de Ciencia e Innovación

Publisher

Elsevier BV

Reference45 articles.

1. Artificial intelligence for diabetic retinopathy screening: a review;Grzybowski;Eye (Basingstoke),2020

2. DR|Graduate: uncertainty-aware deep learning-based diabetic retinopathy grading in eye fundus images;Araújo;Med. Image Anal.,2020

3. Deep learning algorithms for detection of diabetic retinopathy in retinal fundus photographs: a systematic review and meta-analysis;Islam;Comput. Methods Programs Biomed.,2020

4. A survey on medical image analysis in diabetic retinopathy;Stolte;Med. Image Anal.,2020

5. Grader Variability and the Importance of Reference Standards for Evaluating Machine Learning Models for Diabetic Retinopathy;Krause;Ophthalmology.,2018

Cited by 2 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Exploring Artificial Intelligence Techniques for Diabetic Retinopathy Detection;Advances in Computational Intelligence and Robotics;2024-08-30

2. Diabetic retinopathy detection using a well-calibrated uncertainty aware convolutional neural network;Multimedia Tools and Applications;2024-07-24

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