These tools will no longer be maintained as of December 31, 2024. Archived website can be found here. PubMed4Hh GitHub repository can be found here. Contact NLM Customer Service if you have questions.
452 related articles for article (PubMed ID: 31932886)
1. Multi-label classification of retinal lesions in diabetic retinopathy for automatic analysis of fundus fluorescein angiography based on deep learning. Pan X; Jin K; Cao J; Liu Z; Wu J; You K; Lu Y; Xu Y; Su Z; Jiang J; Yao K; Ye J Graefes Arch Clin Exp Ophthalmol; 2020 Apr; 258(4):779-785. PubMed ID: 31932886 [TBL] [Abstract][Full Text] [Related]
2. Automatic detection of non-perfusion areas in diabetic macular edema from fundus fluorescein angiography for decision making using deep learning. Jin K; Pan X; You K; Wu J; Liu Z; Cao J; Lou L; Xu Y; Su Z; Yao K; Ye J Sci Rep; 2020 Sep; 10(1):15138. PubMed ID: 32934283 [TBL] [Abstract][Full Text] [Related]
3. End-to-end diabetic retinopathy grading based on fundus fluorescein angiography images using deep learning. Gao Z; Jin K; Yan Y; Liu X; Shi Y; Ge Y; Pan X; Lu Y; Wu J; Wang Y; Ye J Graefes Arch Clin Exp Ophthalmol; 2022 May; 260(5):1663-1673. PubMed ID: 35066704 [TBL] [Abstract][Full Text] [Related]
4. Automatic interpretation and clinical evaluation for fundus fluorescein angiography images of diabetic retinopathy patients by deep learning. Gao Z; Pan X; Shao J; Jiang X; Su Z; Jin K; Ye J Br J Ophthalmol; 2023 Nov; 107(12):1852-1858. PubMed ID: 36171054 [TBL] [Abstract][Full Text] [Related]
5. Deep Convolutional Neural Network-Based Early Automated Detection of Diabetic Retinopathy Using Fundus Image. Xu K; Feng D; Mi H Molecules; 2017 Nov; 22(12):. PubMed ID: 29168750 [TBL] [Abstract][Full Text] [Related]
6. Automatic Grading System for Diabetic Retinopathy Diagnosis Using Deep Learning Artificial Intelligence Software. Wang XN; Dai L; Li ST; Kong HY; Sheng B; Wu Q Curr Eye Res; 2020 Dec; 45(12):1550-1555. PubMed ID: 32410471 [No Abstract] [Full Text] [Related]
7. Multi-label classification of fundus images based on graph convolutional network. Cheng Y; Ma M; Li X; Zhou Y BMC Med Inform Decis Mak; 2021 Jul; 21(Suppl 2):82. PubMed ID: 34330270 [TBL] [Abstract][Full Text] [Related]
8. A deep learning framework for the early detection of multi-retinal diseases. Ejaz S; Baig R; Ashraf Z; Alnfiai MM; Alnahari MM; Alotaibi RM PLoS One; 2024; 19(7):e0307317. PubMed ID: 39052616 [TBL] [Abstract][Full Text] [Related]
9. A super-resolution method-based pipeline for fundus fluorescein angiography imaging. Jiang Z; Yu Z; Feng S; Huang Z; Peng Y; Guo J; Ren Q; Lu Y Biomed Eng Online; 2018 Sep; 17(1):125. PubMed ID: 30231879 [TBL] [Abstract][Full Text] [Related]
10. The diagnostic accuracy of an intelligent and automated fundus disease image assessment system with lesion quantitative function (SmartEye) in diabetic patients. Xu Y; Wang Y; Liu B; Tang L; Lv L; Ke X; Ling S; Lu L; Zou H BMC Ophthalmol; 2019 Aug; 19(1):184. PubMed ID: 31412800 [TBL] [Abstract][Full Text] [Related]
11. Ultrawide-field fluorescein angiography for evaluation of diabetic retinopathy. Kong M; Lee MY; Ham DI Korean J Ophthalmol; 2012 Dec; 26(6):428-31. PubMed ID: 23204797 [TBL] [Abstract][Full Text] [Related]
12. Evaluation of automated fundus photograph analysis algorithms for detecting microaneurysms, haemorrhages and exudates, and of a computer-assisted diagnostic system for grading diabetic retinopathy. Dupas B; Walter T; Erginay A; Ordonez R; Deb-Joardar N; Gain P; Klein JC; Massin P Diabetes Metab; 2010 Jun; 36(3):213-20. PubMed ID: 20219404 [TBL] [Abstract][Full Text] [Related]
13. Attention-based deep learning framework for automatic fundus image processing to aid in diabetic retinopathy grading. Romero-Oraá R; Herrero-Tudela M; López MI; Hornero R; García M Comput Methods Programs Biomed; 2024 Jun; 249():108160. PubMed ID: 38583290 [TBL] [Abstract][Full Text] [Related]
14. A convolutional neural network for the screening and staging of diabetic retinopathy. Shaban M; Ogur Z; Mahmoud A; Switala A; Shalaby A; Abu Khalifeh H; Ghazal M; Fraiwan L; Giridharan G; Sandhu H; El-Baz AS PLoS One; 2020; 15(6):e0233514. PubMed ID: 32569310 [TBL] [Abstract][Full Text] [Related]
15. Development and Validation of Deep Learning Models for Screening Multiple Abnormal Findings in Retinal Fundus Images. Son J; Shin JY; Kim HD; Jung KH; Park KH; Park SJ Ophthalmology; 2020 Jan; 127(1):85-94. PubMed ID: 31281057 [TBL] [Abstract][Full Text] [Related]
16. Deep learning based computer-aided diagnosis systems for diabetic retinopathy: A survey. Asiri N; Hussain M; Al Adel F; Alzaidi N Artif Intell Med; 2019 Aug; 99():101701. PubMed ID: 31606116 [TBL] [Abstract][Full Text] [Related]
17. Automated detection of diabetic retinopathy on digital fundus images. Sinthanayothin C; Boyce JF; Williamson TH; Cook HL; Mensah E; Lal S; Usher D Diabet Med; 2002 Feb; 19(2):105-12. PubMed ID: 11874425 [TBL] [Abstract][Full Text] [Related]
18. Automatic detection of diabetic retinopathy and age-related macular degeneration in digital fundus images. Agurto C; Barriga ES; Murray V; Nemeth S; Crammer R; Bauman W; Zamora G; Pattichis MS; Soliz P Invest Ophthalmol Vis Sci; 2011 Jul; 52(8):5862-71. PubMed ID: 21666234 [TBL] [Abstract][Full Text] [Related]
19. Algorithms for the automated detection of diabetic retinopathy using digital fundus images: a review. Faust O; Acharya U R; Ng EY; Ng KH; Suri JS J Med Syst; 2012 Feb; 36(1):145-57. PubMed ID: 20703740 [TBL] [Abstract][Full Text] [Related]
20. Automatic detection of 39 fundus diseases and conditions in retinal photographs using deep neural networks. Cen LP; Ji J; Lin JW; Ju ST; Lin HJ; Li TP; Wang Y; Yang JF; Liu YF; Tan S; Tan L; Li D; Wang Y; Zheng D; Xiong Y; Wu H; Jiang J; Wu Z; Huang D; Shi T; Chen B; Yang J; Zhang X; Luo L; Huang C; Zhang G; Huang Y; Ng TK; Chen H; Chen W; Pang CP; Zhang M Nat Commun; 2021 Aug; 12(1):4828. PubMed ID: 34376678 [TBL] [Abstract][Full Text] [Related] [Next] [New Search]