%0 Conference Paper %F Oral %T Classifying DME vs Normal SD-OCT volumes: A review %+ Laboratoire Electronique, Informatique et Image [UMR6306] (Le2i) %+ Singapore Eye Research Institute, Singapore National Eye Centre %+ Centre for Intelligent Signal and Imaging Research [Petronas] (CISIR) %A Massich, Joan %A Rastgoo, Mojdeh %A Lemaître, Guillaume %A Cheung, Carol, y %A Wong, Tien, y %A Sidibé, Désiré %A Mériaudeau, Fabrice %< avec comité de lecture %B 23rd International Conference on Pattern Recognition %C Cancun, Mexico %8 2016-12-04 %D 2016 %K Diabetic Macular Edema (DME) %K Spectral Domain OCT (SD-OCT) %K Machine Learning (ML) %K benchmark %Z Engineering Sciences [physics]/Signal and Image processingConference papers %X This article reviews the current state of automatic classification methodologies to identify Diabetic Macular Edema (DME) versus normal subjects based on Spectral Domain OCT (SD-OCT) data. Addressing this classification problem has valuable interest since early detection and treatment of DME play a major role to prevent eye adverse effects such as blindness. The main contribution of this article is to cover the lack of a public dataset and benchmark suited for classifying DME and normal SD-OCT volumes, providing our own implementation of the most relevant methodologies in the literature. Subsequently, 6 different methods were implemented and evaluated using this common benchmark and dataset to produce reliable comparison. %G English %2 https://u-bourgogne.hal.science/hal-01376469/document %2 https://u-bourgogne.hal.science/hal-01376469/file/main%2811%29.pdf %L hal-01376469 %U https://u-bourgogne.hal.science/hal-01376469 %~ UNIV-BOURGOGNE %~ CNRS %~ ENSAM %~ LE2I %~ AGREENIUM %~ ARTS-ET-METIERS-SCIENCES-ET-TECHNOLOGIES %~ HESAM %~ HESAM-ENSAM %~ INSTITUT-AGRO