Microaneurysms segmentation with a U-Net based on recurrent residual convolutional neural network
Author:
Affiliation:
1. Beijing University of Posts and Telecommunications, Haidian District, Beijing
2. Peking University, Haidian District, Beijing
3. Peking University First Hospital, Xicheng District, Beijing
Publisher
SPIE-Intl Soc Optical Eng
Subject
Radiology, Nuclear Medicine and imaging
Reference30 articles.
1. Screening for sight-threatening diabetic retinopathy: comparison of fundus photography with automated color contrast threshold test
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3. The Prevalence of and Factors Associated With Diabetic Retinopathy in the Australian Population
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5. Diabetic retinopathy techniques in retinal images: A review
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1. Ensembling U-Nets for microaneurysm segmentation in optical coherence tomography angiography in patients with diabetic retinopathy;Scientific Reports;2024-09-14
2. APNet: Adaptive Patch-based Network for Microaneurysm Detection in Fundus Images;2024 IEEE 33rd International Symposium on Industrial Electronics (ISIE);2024-06-18
3. Ens5B-UNet for Improved Microaneurysms Segmentation in Retinal Images;2024 International Conference on Science, Engineering and Business for Driving Sustainable Development Goals (SEB4SDG);2024-04-02
4. Advances in retinal microaneurysms detection, segmentation and datasets for the diagnosis of diabetic retinopathy: a systematic literature review;Multimedia Tools and Applications;2024-02-13
5. Just Noticeable Difference-Guided Multipath Deep Attention Network for Microaneurysm Segmentation in Fundus Images;IEEE Transactions on Instrumentation and Measurement;2024
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