Multi-class multi-label ophthalmological disease detection using transfer learning based convolutional neural network
暂无分享,去创建一个
[1] Celia Shahnaz,et al. Source and Camera Independent Ophthalmic Disease Recognition from Fundus Image Using Neural Network , 2019, 2019 IEEE International Conference on Signal Processing, Information, Communication & Systems (SPICSCON).
[2] Amit Kumar Singh,et al. Image Processing Based Automated Glaucoma Detection Techniques and Role of De-Noising: A Technical Survey , 2019, Handbook of Multimedia Information Security.
[3] Matthew D. Davis,et al. The Age-Related Eye Disease Study Severity Scale for Age-Related Macular Degeneration , 2015 .
[4] Kaamran Raahemifar,et al. Retinal fundus images for glaucoma analysis: the RIGA dataset , 2018, Medical Imaging.
[5] Greg Russell,et al. DR HAGIS—a fundus image database for the automatic extraction of retinal surface vessels from diabetic patients , 2017, Journal of medical imaging.
[6] Bunyarit Uyyanonvara,et al. Blood vessel segmentation methodologies in retinal images - A survey , 2012, Comput. Methods Programs Biomed..
[7] B. Zinman,et al. Diabetic retinopathy and diabetic macular edema: pathophysiology, screening, and novel therapies. , 2003, Diabetes care.
[8] M. C. Leske,et al. Reduction of intraocular pressure and glaucoma progression: results from the Early Manifest Glaucoma Trial. , 2002, Archives of ophthalmology.
[9] Nan Wu,et al. Deep Neural Networks Improve Radiologists’ Performance in Breast Cancer Screening , 2019, IEEE Transactions on Medical Imaging.
[10] Pietro Perona,et al. Microsoft COCO: Common Objects in Context , 2014, ECCV.
[11] Mamta Juneja,et al. Survey of Classification Approaches for Glaucoma Diagnosis from Retinal Images , 2018 .
[12] J Katz,et al. A population-based evaluation of glaucoma screening: the Baltimore Eye Survey. , 1991, American journal of epidemiology.
[13] Guy Cazuguel,et al. FEEDBACK ON A PUBLICLY DISTRIBUTED IMAGE DATABASE: THE MESSIDOR DATABASE , 2014 .
[14] Cheng Li,et al. Dense Correlation Network for Automated Multi-Label Ocular Disease Detection with Paired Color Fundus Photographs , 2020, 2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI).
[15] Mussarat Yasmin,et al. A Review on Recent Developments for Detection of Diabetic Retinopathy , 2016, Scientifica.
[16] Kaamran Raahemifar,et al. Optic Disc and Optic Cup Segmentation Methodologies for Glaucoma Image Detection: A Survey , 2015, Journal of ophthalmology.
[17] Lucas M Bachmann,et al. Automated deep learning design for medical image classification by health-care professionals with no coding experience: a feasibility study. , 2019, The Lancet. Digital health.
[18] Yu Sun,et al. Multi-Input Convolutional Neural Network for Flower Grading , 2017, J. Electr. Comput. Eng..
[19] Shin'ichi Satoh,et al. Fundus Image Classification and Retinal Disease Localization with Limited Supervision , 2019, ACPR.
[20] Tien Yin Wong,et al. ORIGA-light: An online retinal fundus image database for glaucoma analysis and research , 2010, 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology.
[21] Pablo Andrés Arbeláez,et al. Automatic diabetic retinopathy classification , 2017, Symposium on Medical Information Processing and Analysis.
[22] R. Klein,et al. Methods for evaluation of retinal microvascular abnormalities associated with hypertension/sclerosis in the Atherosclerosis Risk in Communities Study. , 1999, Ophthalmology.
[23] Geoffrey E. Hinton,et al. ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.
[24] Joni-Kristian Kämäräinen,et al. The DIARETDB1 Diabetic Retinopathy Database and Evaluation Protocol , 2007, BMVC.
[25] U. Rajendra Acharya,et al. Automated detection of retinal health using PHOG and SURF features extracted from fundus images , 2018, Applied Intelligence.
[26] Yoshua Bengio,et al. Gradient-based learning applied to document recognition , 1998, Proc. IEEE.
[27] A.D. Hoover,et al. Locating blood vessels in retinal images by piecewise threshold probing of a matched filter response , 2000, IEEE Transactions on Medical Imaging.
[28] Michael D. Ober,et al. Ophthalmic fundus imaging: today and beyond. , 2004, American journal of ophthalmology.
[29] Matthew B. Blaschko,et al. Convolutional neural network transfer for automated glaucoma identification , 2017, Symposium on Medical Information Processing and Analysis.
[30] Junhao Wen,et al. Fundus Image Classification Using VGG-19 Architecture with PCA and SVD , 2018, Symmetry.
[31] R. Klein,et al. Prospective cohort study of retinal vessel diameters and risk of hypertension , 2004, BMJ : British Medical Journal.
[32] M. Sonka,et al. Retinal Imaging and Image Analysis. , 2010, IEEE transactions on medical imaging.
[33] Jayanthi Sivaswamy,et al. A Comprehensive Retinal Image Dataset for the Assessment of Glaucoma from the Optic Nerve Head Analysis , 2015 .