Fetching the paper…
Reading the bibliography…
This paper addresses the problem of identifying two main types of lesions - Exudates and Microaneurysms - caused by Diabetic Retinopathy (DR) in the eyes of diabetic patients.
G. Gardner, D. Keating, T. H. Williamson, and A. T. Elliott, “Automatic detection of diabetic retinopathy using an artificial neural network: a screening tool.,” British journal of Ophthalmology
1996
Earlier work this paper cites.
Z. Liu, C. Opas, and S. M. Krishnan, “Automatic image analysis of fundus photograph,” in Proceedings of the 19th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.’Magnificent Milestones and Emerging Opportunities in Medical Engineering’(Cat. No. 97CH36136)
1997
Earlier work this paper cites.
C. Sinthanayothin, J. F. Boyce, H. L. Cook, and T. H. Williamson, “Automated localisation of the optic disc, fovea, and retinal blood vessels from digital colour fundus images,” British journal of ophthalmology
1999
Earlier work this paper cites.
B. M. Ege, O. K. Hejlesen, O. V. Larsen, K. Møller, B. Jennings, D. Kerr, and D. A. Cavan, “Screening for diabetic retinopathy using computer based image analysis and statistical classification,” Computer methods and programs in biomedicine
2000
Earlier work this paper cites.
G. Bradski, “The OpenCV Library,” Dr. Dobb’s Journal of Software Tools
2000
Earlier work this paper cites.
D. Usher, M. Dumskyj, M. Himaga, T. H. Williamson, S. Nussey, and J. Boyce, “Automated detection of diabetic retinopathy in digital retinal images: a tool for diabetic retinopathy screening,” Diabetic Medicine
2004
Earlier work this paper cites.
P. Massin, A. Chabouis, A. Erginay, C. Viens-Bitker, A. Lecleire-Collet, T. Meas, P.-J. Guillausseau, G. Choupot, B. André, and P. Denormandie, “Ophdiat©: A telemedical network screening system for diabetic retinopathy in the île-de-france,” Diabetes & metabolism
2008
Earlier work this paper cites.
R. Acharya, C. K. Chua, E. Ng, W. Yu, and C. Chee, “Application of higher order spectra for the identification of diabetes retinopathy stages,” Journal of medical systems
2008
Earlier work this paper cites.
A. Sopharak, M. N. Dailey, B. Uyyanonvara, S. Barman, T. Williamson, K. T. Nwe, and Y. A. Moe, “Machine learning approach to automatic exudate detection in retinal images from diabetic patients,” Journal of Modern optics
2010
Earlier work this paper cites.
A. Krizhevsky, I. Sutskever, and G. E. Hinton, “Imagenet classification with deep convolutional neural networks,” in Advances in neural information processing systems
2012
Earlier work this paper cites.
E. Decencière, G. Cazuguel, X. Zhang, G. Thibault, J.-C. Klein, F. Meyer, B. Marcotegui, G. Quellec, M. Lamard, R. Danno, et al
2013
Earlier work this paper cites.
R. Priya and P. Aruna, “Diagnosis of diabetic retinopathy using machine learning techniques,” ICTACT Journal on soft computing
2013
Cited alongside, same era.
S. Roychowdhury, D. D. Koozekanani, and K. K. Parhi, “Dream: diabetic retinopathy analysis using machine learning,” IEEE journal of biomedical and health informatics
2013
Cited alongside, same era.
P. Adarsh and D. Jeyakumari, “Multiclass svm-based automated diagnosis of diabetic retinopathy,” in 2013 International Conference on Communication and Signal Processing
2013
Cited alongside, same era.
B. Antal and A. Hajdu, “An ensemble-based system for automatic screening of diabetic retinopathy,” Knowledge-based systems
2014
Cited alongside, same era.
R. Girshick, J. Donahue, T. Darrell, and J. Malik, “Rich feature hierarchies for accurate object detection and semantic segmentation,” in Proceedings of the IEEE conference on computer vision and pattern recognition
H. Pratt, F. Coenen, D. M. Broadbent, S. P. Harding, and Y. Zheng, “Convolutional neural networks for diabetic retinopathy,” Procedia Computer Science
2016
Later among the works it cites.
K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in Proceedings of the IEEE conference on computer vision and pattern recognition
2016
Later among the works it cites.
R. Gargeya and T. Leng, “Automated identification of diabetic retinopathy using deep learning,” Ophthalmology
2017
Later among the works it cites.
K. He, G. Gkioxari, P. Dollár, and R. Girshick, “Mask r-cnn,” in Proceedings of the IEEE international conference on computer vision
2017
Later among the works it cites.
A. Das, S. Datta, G. Gkioxari, S. Lee, D. Parikh, and D. Batra, “Embodied question answering,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
2014
Cited alongside, same era.
2014
Cited alongside, same era.
J. Lachure, A. Deorankar, S. Lachure, S. Gupta, and R. Jadhav, “Diabetic retinopathy using morphological operations and machine learning,” in 2015 IEEE International Advance Computing Conference (IACC)
2015
Cited alongside, same era.
R. Girshick, “Fast r-cnn,” in Proceedings of the IEEE international conference on computer vision
2015
Cited alongside, same era.
S. Ren, K. He, R. Girshick, and J. Sun, “Faster r-cnn: Towards real-time object detection with region proposal networks,” in Advances in neural information processing systems
2015
Cited alongside, same era.
K. Bhatia, S. Arora, and R. Tomar, “Diagnosis of diabetic retinopathy using machine learning classification algorithm,” in 2016 2nd International Conference on Next Generation Computing Technologies (NGCT)
2016
Cited alongside, same era.
V. Gulshan, L. Peng, M. Coram, M. C. Stumpe, D. Wu, A. Narayanaswamy, S. Venugopalan, K. Widner, T. Madams, J. Cuadros, et al
2016
Cited alongside, same era.
2018
Later among the works it cites.
S. Amirian, K. Rasheed, T. R. Taha, and H. R. Arabnia, “Image captioning with generative adversarial network,” in 2019 International Conference on Computational Science and Computational Intelligence (CSCI)
2019
Later among the works it cites.
F. G. Mohammadi, H. R. Arabnia, and M. H. Amini, “On parameter tuning in meta-learning for computer vision,” in 2019 International Conference on Computational Science and Computational Intelligence (CSCI)
2019
Later among the works it cites.
S. Xie, A. Kirillov, R. Girshick, and K. He, “Exploring randomly wired neural networks for image recognition,” in Proceedings of the IEEE International Conference on Computer Vision
2019
Later among the works it cites.
F. Shenavarmasouleh and H. Arabnia, “Causes of misleading statistics and research results irreproducibility: A concise review,” in 2019 International Conference on Computational Science and Computational Intelligence (CSCI)
2019
Later among the works it cites.
N. Soans, E. Asali, Y. Hong, and P. Doshi, “Sa-net: Robust state-action recognition for learning from observations,” in IEEE International Conference on Robotics and Automation (ICRA)
2020
Closest in time.