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Items: 1 to 20 of 101

1.

IDRiD: Diabetic Retinopathy - Segmentation and Grading Challenge.

Porwal P, Pachade S, Kokare M, Deshmukh G, Son J, Bae W, Liu L, Wang J, Liu X, Gao L, Wu T, Xiao J, Wang F, Yin B, Wang Y, Danala G, He L, Choi YH, Lee YC, Jung SH, Li Z, Sui X, Wu J, Li X, Zhou T, Toth J, Baran A, Kori A, Chennamsetty SS, Safwan M, Alex V, Lyu X, Cheng L, Chu Q, Li P, Ji X, Zhang S, Shen Y, Dai L, Saha O, Sathish R, Melo T, Araújo T, Harangi B, Sheng B, Fang R, Sheet D, Hajdu A, Zheng Y, Mendonça AM, Zhang S, Campilho A, Zheng B, Shen D, Giancardo L, Quellec G, Mériaudeau F.

Med Image Anal. 2020 Jan;59:101561. doi: 10.1016/j.media.2019.101561. Epub 2019 Oct 3.

PMID:
31671320
2.

Error occurred: cannot get document summary

PMID:
31946452

3.

Efficient multi-kernel multi-instance learning using weakly supervised and imbalanced data for diabetic retinopathy diagnosis.

Cao P, Ren F, Wan C, Yang J, Zaiane O.

Comput Med Imaging Graph. 2018 Nov;69:112-124. doi: 10.1016/j.compmedimag.2018.08.008. Epub 2018 Aug 25.

PMID:
30237145
4.

CANet: Cross-disease Attention Network for Joint Diabetic Retinopathy and Diabetic Macular Edema Grading.

Li X, Hu X, Yu L, Zhu L, Fu CW, Heng PA.

IEEE Trans Med Imaging. 2019 Nov 6. doi: 10.1109/TMI.2019.2951844. [Epub ahead of print]

PMID:
31714219
5.

Artificial Intelligence in Diabetic Eye Disease Screening.

Cheung CY, Tang F, Ting DSW, Tan GSW, Wong TY.

Asia Pac J Ophthalmol (Phila). 2019 Apr 24. doi: 10.22608/APO.201976. [Epub ahead of print]

PMID:
31016915
6.

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. doi: 10.1080/03091902.2019.1576790. Epub 2019 Jun 14. Review.

PMID:
31198073
7.

Automated Identification of Diabetic Retinopathy Using Deep Learning.

Gargeya R, Leng T.

Ophthalmology. 2017 Jul;124(7):962-969. doi: 10.1016/j.ophtha.2017.02.008. Epub 2017 Mar 27.

PMID:
28359545
8.

Retinal images benchmark for the detection of diabetic retinopathy and clinically significant macular edema (CSME).

Noor-Ul-Huda M, Tehsin S, Ahmed S, Niazi FAK, Murtaza Z.

Biomed Tech (Berl). 2019 May 27;64(3):297-307. doi: 10.1515/bmt-2018-0098.

PMID:
30055096
9.

SCREEN-DR: Collaborative platform for diabetic retinopathy.

Pedrosa M, Silva JM, Silva JF, Matos S, Costa C.

Int J Med Inform. 2018 Dec;120:137-146. doi: 10.1016/j.ijmedinf.2018.10.005. Epub 2018 Oct 18.

PMID:
30409338
10.

A Machine Learning Ensemble Classifier for Early Prediction of Diabetic Retinopathy.

S K S, P A.

J Med Syst. 2017 Nov 9;41(12):201. doi: 10.1007/s10916-017-0853-x.

PMID:
29124453
11.

Simple methods for segmentation and measurement of diabetic retinopathy lesions in retinal fundus images.

Köse C, Sevik U, Ikibaş C, Erdöl H.

Comput Methods Programs Biomed. 2012 Aug;107(2):274-93. doi: 10.1016/j.cmpb.2011.06.007. Epub 2011 Jul 14.

PMID:
21757250
12.

A Survey on Intelligent Screening for Diabetic Retinopathy.

Dai YL, Zhu CZ, Shan X, Cheng ZZ, Zou BJ.

Chin Med Sci J. 2019 Jun 30;34(2):120-132. doi: 10.24920/003587.

PMID:
31315753
13.

Automated Diabetic Retinopathy Screening and Monitoring Using Retinal Fundus Image Analysis.

Bhaskaranand M, Ramachandra C, Bhat S, Cuadros J, Nittala MG, Sadda S, Solanki K.

J Diabetes Sci Technol. 2016 Feb 16;10(2):254-61. doi: 10.1177/1932296816628546.

14.

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. doi: 10.1016/j.artmed.2019.07.009. Epub 2019 Aug 7. Review.

PMID:
31606116
15.

Algorithms for red lesion detection in Diabetic Retinopathy: A review.

Biyani RS, Patre BM.

Biomed Pharmacother. 2018 Nov;107:681-688. doi: 10.1016/j.biopha.2018.07.175. Epub 2018 Aug 18. Review.

PMID:
30130729
16.

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. doi: 10.1016/j.media.2017.04.012. Epub 2017 Apr 28.

PMID:
28511066
17.

Automated detection of diabetic retinopathy in retinal images.

Valverde C, Garcia M, Hornero R, Lopez-Galvez MI.

Indian J Ophthalmol. 2016 Jan;64(1):26-32. doi: 10.4103/0301-4738.178140. Review.

18.

An automated method for accurate vessel segmentation.

Yang X, Liu C, Le Minh H, Wang Z, Chien A, Cheng KT.

Phys Med Biol. 2017 May 7;62(9):3757-3778. doi: 10.1088/1361-6560/aa6418. Epub 2017 Apr 6.

PMID:
28384126
19.

Screening for Diabetic Retinopathy Using a Portable, Noncontact, Nonmydriatic Handheld Retinal Camera.

Zhang W, Nicholas P, Schuman SG, Allingham MJ, Faridi A, Suthar T, Cousins SW, Prakalapakorn SG.

J Diabetes Sci Technol. 2017 Jan;11(1):128-134. doi: 10.1177/1932296816658902. Epub 2016 Jul 11.

20.

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. doi: 10.1007/s11517-017-1638-6. Epub 2017 Mar 28.

PMID:
28353133

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