349 related articles for article (PubMed ID: 29295157)
21. Deep image mining for diabetic retinopathy screening.
Quellec G; Charrière K; Boudi Y; Cochener B; Lamard M
Med Image Anal; 2017 Jul; 39():178-193. PubMed ID: 28511066
[TBL] [Abstract][Full Text] [Related]
22. MediDRNet: Tackling category imbalance in diabetic retinopathy classification with dual-branch learning and prototypical contrastive learning.
Teng S; Wang B; Yang F; Yi X; Zhang X; Sun Y
Comput Methods Programs Biomed; 2024 Aug; 253():108230. PubMed ID: 38810377
[TBL] [Abstract][Full Text] [Related]
23. Fully automated diabetic retinopathy screening using morphological component analysis.
Imani E; Pourreza HR; Banaee T
Comput Med Imaging Graph; 2015 Jul; 43():78-88. PubMed ID: 25863517
[TBL] [Abstract][Full Text] [Related]
24. Detection of exudates in fundus photographs using deep neural networks and anatomical landmark detection fusion.
Prentašić P; Lončarić S
Comput Methods Programs Biomed; 2016 Dec; 137():281-292. PubMed ID: 28110732
[TBL] [Abstract][Full Text] [Related]
25. Contrastive self-supervised learning for diabetic retinopathy early detection.
Ouyang J; Mao D; Guo Z; Liu S; Xu D; Wang W
Med Biol Eng Comput; 2023 Sep; 61(9):2441-2452. PubMed ID: 37119374
[TBL] [Abstract][Full Text] [Related]
26. Minimized Computations of Deep Learning Technique for Early Diagnosis of Diabetic Retinopathy Using IoT-Based Medical Devices.
Ayoub S; Khan MA; Jadhav VP; Anandaram H; Anil Kumar TC; Reegu FA; Motwani D; Shrivastava AK; Berhane R
Comput Intell Neurosci; 2022; 2022():7040141. PubMed ID: 36156979
[TBL] [Abstract][Full Text] [Related]
27. 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]
28. Automatic Grading of Retinal Blood Vessel in Deep Retinal Image Diagnosis.
Maji D; Sekh AA
J Med Syst; 2020 Sep; 44(10):180. PubMed ID: 32870389
[TBL] [Abstract][Full Text] [Related]
29. 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]
30. Assessment of Computer-Assisted Screening Technology for Diabetic Retinopathy Screening in India - Preliminary Results and Recommendations from a Pilot Study.
John S; Ram K; Sivaprakasam M; Raman R
Stud Health Technol Inform; 2016; 231():74-81. PubMed ID: 27782018
[TBL] [Abstract][Full Text] [Related]
31. Diabetic retinopathy screening in the emerging era of artificial intelligence.
Grauslund J
Diabetologia; 2022 Sep; 65(9):1415-1423. PubMed ID: 35639120
[TBL] [Abstract][Full Text] [Related]
32. Non-proliferative diabetic retinopathy symptoms detection and classification using neural network.
Al-Jarrah MA; Shatnawi H
J Med Eng Technol; 2017 Aug; 41(6):498-505. PubMed ID: 28786703
[TBL] [Abstract][Full Text] [Related]
33. A review on computer-aided recent developments for automatic detection of diabetic retinopathy.
Randive SN; Senapati RK; Rahulkar AD
J Med Eng Technol; 2019 Feb; 43(2):87-99. PubMed ID: 31198073
[TBL] [Abstract][Full Text] [Related]
34. Automatic recognition of severity level for diagnosis of diabetic retinopathy using deep visual features.
Abbas Q; Fondon I; Sarmiento A; Jiménez S; Alemany P
Med Biol Eng Comput; 2017 Nov; 55(11):1959-1974. PubMed ID: 28353133
[TBL] [Abstract][Full Text] [Related]
35. Study of the Application of Deep Convolutional Neural Networks (CNNs) in Processing Sensor Data and Biomedical Images.
Hu W; Zhang Y; Li L
Sensors (Basel); 2019 Aug; 19(16):. PubMed ID: 31426516
[TBL] [Abstract][Full Text] [Related]
36. 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]
37. Grader Variability and the Importance of Reference Standards for Evaluating Machine Learning Models for Diabetic Retinopathy.
Krause J; Gulshan V; Rahimy E; Karth P; Widner K; Corrado GS; Peng L; Webster DR
Ophthalmology; 2018 Aug; 125(8):1264-1272. PubMed ID: 29548646
[TBL] [Abstract][Full Text] [Related]
38. 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]
39. Microaneurysm detection using fully convolutional neural networks.
Chudzik P; Majumdar S; Calivá F; Al-Diri B; Hunter A
Comput Methods Programs Biomed; 2018 May; 158():185-192. PubMed ID: 29544784
[TBL] [Abstract][Full Text] [Related]
40. Transfer Learning for Automated OCTA Detection of Diabetic Retinopathy.
Le D; Alam M; Yao CK; Lim JI; Hsieh YT; Chan RVP; Toslak D; Yao X
Transl Vis Sci Technol; 2020 Jul; 9(2):35. PubMed ID: 32855839
[TBL] [Abstract][Full Text] [Related]
[Previous] [Next] [New Search]