451 related articles for article (PubMed ID: 32855845)
1. Leveraging Multimodal Deep Learning Architecture with Retina Lesion Information to Detect Diabetic Retinopathy.
Tseng VS; Chen CL; Liang CM; Tai MC; Liu JT; Wu PY; Deng MS; Lee YW; Huang TY; Chen YH
Transl Vis Sci Technol; 2020 Jul; 9(2):41. PubMed ID: 32855845
[TBL] [Abstract][Full Text] [Related]
2. Automated Identification of Diabetic Retinopathy Using Deep Learning.
Gargeya R; Leng T
Ophthalmology; 2017 Jul; 124(7):962-969. PubMed ID: 28359545
[TBL] [Abstract][Full Text] [Related]
3. Validation of Deep Convolutional Neural Network-based algorithm for detection of diabetic retinopathy - Artificial intelligence versus clinician for screening.
Shah P; Mishra DK; Shanmugam MP; Doshi B; Jayaraj H; Ramanjulu R
Indian J Ophthalmol; 2020 Feb; 68(2):398-405. PubMed ID: 31957737
[TBL] [Abstract][Full Text] [Related]
4. An interpretable multiple-instance approach for the detection of referable diabetic retinopathy in fundus images.
Papadopoulos A; Topouzis F; Delopoulos A
Sci Rep; 2021 Jul; 11(1):14326. PubMed ID: 34253799
[TBL] [Abstract][Full Text] [Related]
5. Using a Deep Learning Algorithm and Integrated Gradients Explanation to Assist Grading for Diabetic Retinopathy.
Sayres R; Taly A; Rahimy E; Blumer K; Coz D; Hammel N; Krause J; Narayanaswamy A; Rastegar Z; Wu D; Xu S; Barb S; Joseph A; Shumski M; Smith J; Sood AB; Corrado GS; Peng L; Webster DR
Ophthalmology; 2019 Apr; 126(4):552-564. PubMed ID: 30553900
[TBL] [Abstract][Full Text] [Related]
6. A Multi-Label Deep Learning Model with Interpretable Grad-CAM for Diabetic Retinopathy Classification.
Jiang H; Xu J; Shi R; Yang K; Zhang D; Gao M; Ma H; Qian W
Annu Int Conf IEEE Eng Med Biol Soc; 2020 Jul; 2020():1560-1563. PubMed ID: 33018290
[TBL] [Abstract][Full Text] [Related]
7. Simple methods for the lesion detection and severity grading of diabetic retinopathy by image processing and transfer learning.
Sugeno A; Ishikawa Y; Ohshima T; Muramatsu R
Comput Biol Med; 2021 Oct; 137():104795. PubMed ID: 34488028
[TBL] [Abstract][Full Text] [Related]
8. Automatic severity grade classification of diabetic retinopathy using deformable ladder Bi attention U-net and deep adaptive CNN.
Durai DBJ; Jaya T
Med Biol Eng Comput; 2023 Aug; 61(8):2091-2113. PubMed ID: 37338737
[TBL] [Abstract][Full Text] [Related]
9. Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs.
Gulshan V; Peng L; Coram M; Stumpe MC; Wu D; Narayanaswamy A; Venugopalan S; Widner K; Madams T; Cuadros J; Kim R; Raman R; Nelson PC; Mega JL; Webster DR
JAMA; 2016 Dec; 316(22):2402-2410. PubMed ID: 27898976
[TBL] [Abstract][Full Text] [Related]
10. Diabetic retinopathy classification based on multipath CNN and machine learning classifiers.
Gayathri S; Gopi VP; Palanisamy P
Phys Eng Sci Med; 2021 Sep; 44(3):639-653. PubMed ID: 34033015
[TBL] [Abstract][Full Text] [Related]
11. UC-stack: a deep learning computer automatic detection system for diabetic retinopathy classification.
Fu Y; Wei Y; Chen S; Chen C; Zhou R; Li H; Qiu M; Xie J; Huang D
Phys Med Biol; 2024 Feb; 69(4):. PubMed ID: 38271723
[No Abstract] [Full Text] [Related]
12. Deep Learning for Automated Diabetic Retinopathy Screening Fused With Heterogeneous Data From EHRs Can Lead to Earlier Referral Decisions.
Hsu MY; Chiou JY; Liu JT; Lee CM; Lee YW; Chou CC; Lo SC; Kornelius E; Yang YS; Chang SY; Liu YC; Huang CN; Tseng VS
Transl Vis Sci Technol; 2021 Aug; 10(9):18. PubMed ID: 34403475
[TBL] [Abstract][Full Text] [Related]
13. Referable diabetic retinopathy identification from eye fundus images with weighted path for convolutional neural network.
Liu YP; Li Z; Xu C; Li J; Liang R
Artif Intell Med; 2019 Aug; 99():101694. PubMed ID: 31606108
[TBL] [Abstract][Full Text] [Related]
14. Diabetic Retinopathy Fundus Image Classification and Lesions Localization System Using Deep Learning.
Alyoubi WL; Abulkhair MF; Shalash WM
Sensors (Basel); 2021 May; 21(11):. PubMed ID: 34073541
[TBL] [Abstract][Full Text] [Related]
15. Application of deep learning image assessment software VeriSee™ for diabetic retinopathy screening.
Hsieh YT; Chuang LM; Jiang YD; Chang TJ; Yang CM; Yang CH; Chan LW; Kao TY; Chen TC; Lin HC; Tsai CH; Chen M
J Formos Med Assoc; 2021 Jan; 120(1 Pt 1):165-171. PubMed ID: 32307321
[TBL] [Abstract][Full Text] [Related]
16. Hemorrhage Detection Based on 3D CNN Deep Learning Framework and Feature Fusion for Evaluating Retinal Abnormality in Diabetic Patients.
Maqsood S; Damaševičius R; Maskeliūnas R
Sensors (Basel); 2021 Jun; 21(11):. PubMed ID: 34205120
[TBL] [Abstract][Full Text] [Related]
17. Non-uniform Label Smoothing for Diabetic Retinopathy Grading from Retinal Fundus Images with Deep Neural Networks.
Galdran A; Chelbi J; Kobi R; Dolz J; Lombaert H; Ben Ayed I; Chakor H
Transl Vis Sci Technol; 2020 Jun; 9(2):34. PubMed ID: 32832207
[TBL] [Abstract][Full Text] [Related]
18. Diabetic retinopathy detection using red lesion localization and convolutional neural networks.
Zago GT; Andreão RV; Dorizzi B; Teatini Salles EO
Comput Biol Med; 2020 Jan; 116():103537. PubMed ID: 31747632
[TBL] [Abstract][Full Text] [Related]
19. An advanced deep learning method to detect and classify diabetic retinopathy based on color fundus images.
Akella PL; Kumar R
Graefes Arch Clin Exp Ophthalmol; 2024 Jan; 262(1):231-247. PubMed ID: 37548671
[TBL] [Abstract][Full Text] [Related]
20. 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]
[Next] [New Search]