258 related articles for article (PubMed ID: 28110732)
1. 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]
2. 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]
3. Weighted ensemble based automatic detection of exudates in fundus photographs.
Prentasic P; Loncaric S
Annu Int Conf IEEE Eng Med Biol Soc; 2014; 2014():138-41. PubMed ID: 25569916
[TBL] [Abstract][Full Text] [Related]
4. Hard exudate detection based on deep model learned information and multi-feature joint representation for diabetic retinopathy screening.
Wang H; Yuan G; Zhao X; Peng L; Wang Z; He Y; Qu C; Peng Z
Comput Methods Programs Biomed; 2020 Jul; 191():105398. PubMed ID: 32092614
[TBL] [Abstract][Full Text] [Related]
5. 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]
6. Decision support system for the detection and grading of hard exudates from color fundus photographs.
Jaafar HF; Nandi AK; Al-Nuaimy W
J Biomed Opt; 2011 Nov; 16(11):116001. PubMed ID: 22112106
[TBL] [Abstract][Full Text] [Related]
7. Detection of Hard Exudates in Colour Fundus Images Using Fuzzy Support Vector Machine-Based Expert System.
Jaya T; Dheeba J; Singh NA
J Digit Imaging; 2015 Dec; 28(6):761-8. PubMed ID: 25822397
[TBL] [Abstract][Full Text] [Related]
8. Fundus images analysis using deep features for detection of exudates, hemorrhages and microaneurysms.
Khojasteh P; Aliahmad B; Kumar DK
BMC Ophthalmol; 2018 Nov; 18(1):288. PubMed ID: 30400869
[TBL] [Abstract][Full Text] [Related]
9. Image analysis of fundus photographs. The detection and measurement of exudates associated with diabetic retinopathy.
Ward NP; Tomlinson S; Taylor CJ
Ophthalmology; 1989 Jan; 96(1):80-6. PubMed ID: 2919052
[TBL] [Abstract][Full Text] [Related]
10. An ensemble deep learning based approach for red lesion detection in fundus images.
Orlando JI; Prokofyeva E; Del Fresno M; Blaschko MB
Comput Methods Programs Biomed; 2018 Jan; 153():115-127. PubMed ID: 29157445
[TBL] [Abstract][Full Text] [Related]
11. Introducing a Novel Layer in Convolutional Neural Network for Automatic Identification of Diabetic Retinopathy.
Khojasteh P; Aliahmad B; Arjunan SP; Kumar DK
Annu Int Conf IEEE Eng Med Biol Soc; 2018 Jul; 2018():5938-5941. PubMed ID: 30441688
[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. Automated detection and differentiation of drusen, exudates, and cotton-wool spots in digital color fundus photographs for diabetic retinopathy diagnosis.
Niemeijer M; van Ginneken B; Russell SR; Suttorp-Schulten MS; Abràmoff MD
Invest Ophthalmol Vis Sci; 2007 May; 48(5):2260-7. PubMed ID: 17460289
[TBL] [Abstract][Full Text] [Related]
14. Exudates Segmentation using Fully Convolutional Neural Network and Auxiliary Codebook.
Chudzik P; Al-Diri B; Caliva F; Ometto G; Hunter A
Annu Int Conf IEEE Eng Med Biol Soc; 2018 Jul; 2018():770-773. PubMed ID: 30440508
[TBL] [Abstract][Full Text] [Related]
15. The SUSTech-SYSU dataset for automated exudate detection and diabetic retinopathy grading.
Lin L; Li M; Huang Y; Cheng P; Xia H; Wang K; Yuan J; Tang X
Sci Data; 2020 Nov; 7(1):409. PubMed ID: 33219237
[TBL] [Abstract][Full Text] [Related]
16. 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]
17. Deep Bayesian baseline for segmenting diabetic retinopathy lesions: Advances and challenges.
Garifullin A; Lensu L; Uusitalo H
Comput Biol Med; 2021 Sep; 136():104725. PubMed ID: 34399196
[TBL] [Abstract][Full Text] [Related]
18. Exudate detection for diabetic retinopathy with convolutional neural networks.
Shuang Yu ; Di Xiao ; Kanagasingam Y
Annu Int Conf IEEE Eng Med Biol Soc; 2017 Jul; 2017():1744-1747. PubMed ID: 29060224
[TBL] [Abstract][Full Text] [Related]
19. Exudate detection in color retinal images for mass screening of diabetic retinopathy.
Zhang X; Thibault G; Decencière E; Marcotegui B; Laÿ B; Danno R; Cazuguel G; Quellec G; Lamard M; Massin P; Chabouis A; Victor Z; Erginay A
Med Image Anal; 2014 Oct; 18(7):1026-43. PubMed ID: 24972380
[TBL] [Abstract][Full Text] [Related]
20. A contribution of image processing to the diagnosis of diabetic retinopathy--detection of exudates in color fundus images of the human retina.
Walter T; Klein JC; Massin P; Erginay A
IEEE Trans Med Imaging; 2002 Oct; 21(10):1236-43. PubMed ID: 12585705
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]