D
Damon Wing Kee Wong
Researcher at Institute for Infocomm Research Singapore
Publications - 192
Citations - 5924
Damon Wing Kee Wong is an academic researcher from Institute for Infocomm Research Singapore. The author has contributed to research in topics: Glaucoma & Optical coherence tomography. The author has an hindex of 30, co-authored 176 publications receiving 4564 citations. Previous affiliations of Damon Wing Kee Wong include Agency for Science, Technology and Research.
Papers
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Journal ArticleDOI
Joint Optic Disc and Cup Segmentation Based on Multi-Label Deep Network and Polar Transformation
TL;DR: Zhang et al. as discussed by the authors proposed a multi-scale input layer, U-shape convolutional network, side-output layer, and multi-label loss function for OD and OC segmentation.
Journal ArticleDOI
Superpixel Classification Based Optic Disc and Optic Cup Segmentation for Glaucoma Screening
Jun Cheng,Jiang Liu,Yanwu Xu,Fengshou Yin,Damon Wing Kee Wong,Ngan Meng Tan,Dacheng Tao,Ching-Yu Cheng,Tin Aung,Tien Yin Wong +9 more
TL;DR: The proposed segmentation methods have been evaluated in a database of 650 images with optic disc and optic cup boundaries manually marked by trained professionals and achieves areas under curve of 0.800 and 0.822 in two data sets, which is higher than other methods.
Book ChapterDOI
DeepVessel: Retinal Vessel Segmentation via Deep Learning and Conditional Random Field
TL;DR: This paper formulate the retinal vessel segmentation problem as a boundary detection task and solve it using a novel deep learning architecture based on a multi-scale and multi-level Convolutional Neural Network with a side-output layer to learn a rich hierarchical representation.
Proceedings ArticleDOI
Glaucoma detection based on deep convolutional neural network
TL;DR: A deep learning (DL) architecture with convolutional neural network for automated glaucoma diagnosis and results show area under curve (AUC) of the receiver operating characteristic curve in glau coma detection at 0.831 and 0.887 in the two databases, much better than state-of-the-art algorithms.
Proceedings ArticleDOI
Retinal vessel segmentation via deep learning network and fully-connected conditional random fields
TL;DR: This paper formulate the vessel segmentation to a boundary detection problem, and utilize the fully convolutional neural networks (CNNs) to generate a vessel probability map that distinguishes the vessels and background in the inadequate contrast region and has robustness to the pathological regions in the fundus image.