Detection of exudates in fundus photographs with imbalanced learning using conditional generative adversarial network
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Title
Detection of exudates in fundus photographs with imbalanced learning using conditional generative adversarial network
Authors
Keywords
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Journal
Biomedical Optics Express
Volume 9, Issue 10, Pages 4863
Publisher
The Optical Society
Online
2018-09-15
DOI
10.1364/boe.9.004863
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Note: Only part of the references are listed.- Effective data generation for imbalanced learning using conditional generative adversarial networks
- (2018) Georgios Douzas et al. EXPERT SYSTEMS WITH APPLICATIONS
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- (2015) Yann LeCun et al. NATURE
- Automated detection of exudates and macula for grading of diabetic macular edema
- (2014) M. Usman Akram et al. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE
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- (2014) Balazs Harangi et al. COMPUTERS IN BIOLOGY AND MEDICINE
- Exudate detection in color retinal images for mass screening of diabetic retinopathy
- (2014) Xiwei Zhang et al. MEDICAL IMAGE ANALYSIS
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- Statistical atlas based exudate segmentation
- (2013) Sharib Ali et al. COMPUTERIZED MEDICAL IMAGING AND GRAPHICS
- Causes of vision loss worldwide, 1990–2010: a systematic analysis
- (2013) Rupert R A Bourne et al. Lancet Global Health
- Splat Feature Classification With Application to Retinal Hemorrhage Detection in Fundus Images
- (2012) Li Tang et al. IEEE TRANSACTIONS ON MEDICAL IMAGING
- Automated Identification of Exudates and Optic Disc Based on Inverse Surface Thresholding
- (2011) Haniza Yazid et al. JOURNAL OF MEDICAL SYSTEMS
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- (2007) Clara I. Sánchez et al. MEDICAL ENGINEERING & PHYSICS
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