TeleOphta: Machine learning and image processing methods for teleophthalmology

By Etienne Decencière, G. Cazuguel, Xiwei Zhang, Guillaume Thibault, Jean-Claude Klein, Fernand Meyer, Beatriz Marcotegui, Gwenolé Quellec, Mathieu Lamard, R. Danno and 6 more. 2013

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Secure Multilayer Perceptron Based On Homomorphic Encryption (2018, methodology): “ysis purposes. In healthcare domain, for example, dierent initiatives aim at sharing medical images and Personal Health Records (PHR) between health professionals or hospitals with the help of cloud [11]. They take advantage of the medical knowledge this volume of data represents so as to develop ne”
FEEDBACK ON A PUBLICLY DISTRIBUTED IMAGE DATABASE: THE MESSIDOR DATABASE (2014, methodology): “Concerning retinal lesions, like microaneurysms and exudates, several databases are available such as DIARETDB0, DIARETDB1 (Kauppi et al. (2007)), HEI-MED (Giancardo et al. (2012)), ROC (Niemeijer et al. (2010)) and e-ophtha (Decencière et al. (2013)).”
IDRiD: Diabetic Retinopathy - Segmentation and Grading Challenge. (2020, unknown): “…image datasets, namely Messidor ( Decencière et al., 2014 ), Kaggle ( Cuadros and Bresnick, 2009 ), ROC ( Niemeijer et al., 2010 ), E-Ophtha ( Decencière et al., 2013 ), DiaretDB ( Kauppi et al., 2012 ), DRIVE ( van Ginneken et al., 2004 ), STARE ( Hoover, 1975 ), ARIA ( Farnell et al., 2008…”
Indian Diabetic Retinopathy Image Dataset (IDRiD): A Database for Diabetic Retinopathy Screening Research (2018, methodology): “Similar pixel level lesion annotations are available in the E-Optha dataset [23].”