Similar articles for PMID: 28894762

Year	Number of Results
2000	1
2003	2
2004	1
2006	1
2007	1
2009	1
2010	2
2011	5
2012	7
2013	1
2014	4
2015	6
2016	5
2017	18
2018	15
2019	13
2020	6
2021	2
2024	0

1

MIRank-KNN: multiple-instance retrieval of clinically relevant diabetic retinopathy images.

Chandakkar PS, Venkatesan R, Li B. Chandakkar PS, et al. J Med Imaging (Bellingham). 2017 Jul;4(3):034003. doi: 10.1117/1.JMI.4.3.034003. Epub 2017 Sep 1. J Med Imaging (Bellingham). 2017. PMID: 28894762 Free PMC article.

2

Classification of diabetic retinopathy images using multi-class multiple-instance learning based on color correlogram features.

Venkatesan R, Chandakkar P, Li B, Li HK. Venkatesan R, et al. Annu Int Conf IEEE Eng Med Biol Soc. 2012;2012:1462-5. doi: 10.1109/EMBC.2012.6346216. Annu Int Conf IEEE Eng Med Biol Soc. 2012. PMID: 23366177

3

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. Araújo T, et al. Med Image Anal. 2020 Jul;63:101715. doi: 10.1016/j.media.2020.101715. Epub 2020 Apr 30. Med Image Anal. 2020. PMID: 32434128

4

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. Noor-Ul-Huda M, et al. Biomed Tech (Berl). 2019 May 27;64(3):297-307. doi: 10.1515/bmt-2018-0098. Biomed Tech (Berl). 2019. PMID: 30055096

5

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. Cao P, et al. Comput Med Imaging Graph. 2018 Nov;69:112-124. doi: 10.1016/j.compmedimag.2018.08.008. Epub 2018 Aug 25. Comput Med Imaging Graph. 2018. PMID: 30237145

6

Sensitivity and specificity of automated analysis of single-field non-mydriatic fundus photographs by Bosch DR Algorithm-Comparison with mydriatic fundus photography (ETDRS) for screening in undiagnosed diabetic retinopathy.

Bawankar P, Shanbhag N, K SS, Dhawan B, Palsule A, Kumar D, Chandel S, Sood S. Bawankar P, et al. PLoS One. 2017 Dec 27;12(12):e0189854. doi: 10.1371/journal.pone.0189854. eCollection 2017. PLoS One. 2017. PMID: 29281690 Free PMC article.

7

Automatic recognition of severity level for diagnosis of diabetic retinopathy using deep visual features.

Abbas Q, Fondon I, Sarmiento A, Jiménez S, Alemany P. Abbas Q, et al. Med Biol Eng Comput. 2017 Nov;55(11):1959-1974. doi: 10.1007/s11517-017-1638-6. Epub 2017 Mar 28. Med Biol Eng Comput. 2017. PMID: 28353133

8

Automatic detection of microaneurysms in retinal fundus images.

Wu B, Zhu W, Shi F, Zhu S, Chen X. Wu B, et al. Comput Med Imaging Graph. 2017 Jan;55:106-112. doi: 10.1016/j.compmedimag.2016.08.001. Epub 2016 Aug 4. Comput Med Imaging Graph. 2017. PMID: 27595214

9

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. Bhaskaranand M, et al. J Diabetes Sci Technol. 2016 Feb 16;10(2):254-61. doi: 10.1177/1932296816628546. J Diabetes Sci Technol. 2016. PMID: 26888972 Free PMC article.

10

Automated lesion detectors in retinal fundus images.

Figueiredo IN, Kumar S, Oliveira CM, Ramos JD, Engquist B. Figueiredo IN, et al. Comput Biol Med. 2015 Nov 1;66:47-65. doi: 10.1016/j.compbiomed.2015.08.008. Epub 2015 Aug 18. Comput Biol Med. 2015. PMID: 26378502

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