Fast Convolutional Neural Network Training Using Selective Data Sampling: Application to Hemorrhage Detection in Color Fundus Images

10.1109/ISBI.2006.1625167

10.1016/j.media.2014.12.001

10.1016/0020-0190(89)90206-8

efraimidis, 2008, Encyclopedia of Algorithms, 1

seff, 2014, 2D view aggregation for lymph node detection using a shallow hierarchy of linear classifiers, Proc MICCAI, 8673, 544

simonyan, 2014, Very deep convolutional networks for large-scale image recognition, ArXiv 14091556

karssemeijer, 2003, Computer-aided detection versus independent double reading of masses on mammograms, Radiology, 227, 192, 10.1148/radiol.2271011962

10.1167/iovs.12-11449

10.1016/j.jacr.2009.06.001

10.1016/j.physa.2011.12.004

10.1006/jcss.1997.1504

10.1109/ISBI.2007.356896

10.1109/ISBI.2015.7163984

10.1023/A:1007330508534

10.1109/TNNLS.2012.2228231

10.1145/1321440.1321461

10.1016/S0140-6736(09)62124-3

10.1109/RBME.2010.2084567

10.1109/ECTICon.2012.6254356

10.1007/s10278-009-9246-0

10.1109/TMI.2008.920619

rasta, 2015, A comparative study on preprocessing techniques in diabetic retinopathy retinal images: illumination correction and contrast enhancement, Journal of medical signals and sensors, 5, 40, 10.4103/2228-7477.150414

krizhevsky, 2012, Imagenet classification with deep convolutional neural networks, Adv Neural Inf Process Syst, 25, 1097

10.1088/1742-6596/274/1/012039

10.1109/ICCV.2015.123

10.1007/978-3-642-40763-5_51

graham, 2015, Kaggle diabetic retinopathy detection competition report

10.1016/j.neunet.2012.02.023

cruz-roa, 2013, A deep learning architecture for image representation, visual interpretability and automated basal-cell carcinoma cancer detection, Proc MICCAI, 8150, 403

10.1007/978-3-319-10470-6_39

10.1016/j.neunet.2014.09.003

10.1007/978-0-387-21579-2_9

10.1038/nature14539

10.1109/TKDE.2008.239

10.1007/s10916-011-9802-2

10.1145/1007730.1007733

10.1109/MIS.2009.36

10.1016/j.compmedimag.2013.05.001

10.1109/CVPR.2015.7298965

10.1109/TMI.2005.843738

srivastava, 2014, Dropout: A simple way to prevent neural networks from overfitting, J Mach Learn Res, 15, 1929

deepa, 2015, Certain investigation of the retinal hemorrhage detection in fundus images, Int J Electron Commun Eng, 2, 29

bergstra, 2012, Random search for hyper-parameter optimization, J Mach Learn Res, 13, 281

10.1046/j.1464-5491.2002.00613.x

10.1109/TPAMI.2012.231

10.1016/0031-3203(81)90009-1

ioffe, 2015, Batch normalization: Accelerating deep network training by reducing internal covariate shift, ArXiv 150203167

10.1109/TMI.2012.2227119