160 related articles for article (PubMed ID: 38583290)
1. 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]
2. DR|GRADUATE: Uncertainty-aware deep learning-based diabetic retinopathy grading in eye fundus images.
Araújo T; Aresta G; Mendonça L; Penas S; Maia C; Carneiro Â; Mendonça AM; Campilho A
Med Image Anal; 2020 Jul; 63():101715. PubMed ID: 32434128
[TBL] [Abstract][Full Text] [Related]
3. Points of interest and visual dictionaries for automatic retinal lesion detection.
Rocha A; Carvalho T; Jelinek HF; Goldenstein S; Wainer J
IEEE Trans Biomed Eng; 2012 Aug; 59(8):2244-53. PubMed ID: 22665502
[TBL] [Abstract][Full Text] [Related]
4. 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]
5. Effective Fundus Image Decomposition for the Detection of Red Lesions and Hard Exudates to Aid in the Diagnosis of Diabetic Retinopathy.
Romero-Oraá R; García M; Oraá-Pérez J; López-Gálvez MI; Hornero R
Sensors (Basel); 2020 Nov; 20(22):. PubMed ID: 33207825
[TBL] [Abstract][Full Text] [Related]
6. Combining transfer learning with retinal lesion features for accurate detection of diabetic retinopathy.
Hassan D; Gill HM; Happe M; Bhatwadekar AD; Hajrasouliha AR; Janga SC
Front Med (Lausanne); 2022; 9():1050436. PubMed ID: 36425113
[TBL] [Abstract][Full Text] [Related]
7. 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]
8. Systematic Comparison of Heatmapping Techniques in Deep Learning in the Context of Diabetic Retinopathy Lesion Detection.
Van Craenendonck T; Elen B; Gerrits N; De Boever P
Transl Vis Sci Technol; 2020 Dec; 9(2):64. PubMed ID: 33403156
[TBL] [Abstract][Full Text] [Related]
9. 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]
10. CauDR: A causality-inspired domain generalization framework for fundus-based diabetic retinopathy grading.
Wei H; Shi P; Miao J; Zhang M; Bai G; Qiu J; Liu F; Yuan W
Comput Biol Med; 2024 Jun; 175():108459. PubMed ID: 38701588
[TBL] [Abstract][Full Text] [Related]
11. Survey on recent developments in automatic detection of diabetic retinopathy.
Bilal A; Sun G; Mazhar S
J Fr Ophtalmol; 2021 Mar; 44(3):420-440. PubMed ID: 33526268
[TBL] [Abstract][Full Text] [Related]
12. 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]
13. Deep learning-based analysis of infrared fundus photography for automated diagnosis of diabetic retinopathy with cataracts.
Xue W; Zhang J; Ma Y; Hou J; Xiao F; Feng R; Zhao R; Zou H
J Cataract Refract Surg; 2023 Oct; 49(10):1043-1048. PubMed ID: 37488748
[TBL] [Abstract][Full Text] [Related]
14. 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]
15. Improved Automatic Grading of Diabetic Retinopathy Using Deep Learning and Principal Component Analysis.
Mohamed E; Elmohsen MA; Basha T
Annu Int Conf IEEE Eng Med Biol Soc; 2021 Nov; 2021():3898-3901. PubMed ID: 34892084
[TBL] [Abstract][Full Text] [Related]
16. Deep Learning Fundus Image Analysis for Diabetic Retinopathy and Macular Edema Grading.
Sahlsten J; Jaskari J; Kivinen J; Turunen L; Jaanio E; Hietala K; Kaski K
Sci Rep; 2019 Jul; 9(1):10750. PubMed ID: 31341220
[TBL] [Abstract][Full Text] [Related]
17. Deep attentive convolutional neural network for automatic grading of imbalanced diabetic retinopathy in retinal fundus images.
Li F; Tang S; Chen Y; Zou H
Biomed Opt Express; 2022 Nov; 13(11):5813-5835. PubMed ID: 36733744
[TBL] [Abstract][Full Text] [Related]
18. An interpretable dual attention network for diabetic retinopathy grading: IDANet.
Bhati A; Gour N; Khanna P; Ojha A; Werghi N
Artif Intell Med; 2024 Mar; 149():102782. PubMed ID: 38462283
[TBL] [Abstract][Full Text] [Related]
19. CoT-XNet: contextual transformer with Xception network for diabetic retinopathy grading.
Zhao S; Wu Y; Tong M; Yao Y; Qian W; Qi S
Phys Med Biol; 2022 Dec; 67(24):. PubMed ID: 36322995
[No Abstract] [Full Text] [Related]
20. A deep learning system for detecting diabetic retinopathy across the disease spectrum.
Dai L; Wu L; Li H; Cai C; Wu Q; Kong H; Liu R; Wang X; Hou X; Liu Y; Long X; Wen Y; Lu L; Shen Y; Chen Y; Shen D; Yang X; Zou H; Sheng B; Jia W
Nat Commun; 2021 May; 12(1):3242. PubMed ID: 34050158
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]