The Ophthalmic Medical Image Analysis International Workshop (OMIA) is held in conjunction with the International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI). The proceedings for OMIA 1–3 are published here.
Retinal Vessel Segmentation from Simple to Difficult
Hanan S. Alghamdi, Hongying Lilian Tang, Saad A. Waheeb, Tunde Peto
Automatic Optic Disc Abnormality Detection in Fundus Images: A Deep Learning Approach
Huiying Liu, Yanwu Xu, Damon W.K. Wong, Augustinus Laude, Tock Ham Lim, Jiang Liu
ACHIKO-D350: A dataset for early AMD detection and drusen segmentation
Oscar Perdomo, Sebastian Otalora, Francisco Rodríguez, John Arevalo, Fabio A. González
A Novel Machine Learning Model Based on Exudate Localization to Detect Diabetic Macular Edema
Baidaa Al-Bander, Waleed Al-Nuaimy, Majid A. Al-Taee, Bryan M. Williams, Yalin Zheng
Diabetic Macular Edema Grading Based on Deep Neural Networks
Ivo Soares, Miguel Castelo-Branco, Antonio M.G. Pinheiro
Microaneurysms detection using a novel neighborhood analysis
Hrvoje Bogunovic, Michael D. Abramoff, Li Zhang, Milan Sonka
Prediction of treatment response from retinal OCT in patients with exudative age-related macular degeneration
Ee Ping Ong, Jun Cheng, Ying Quan, Guozhen Xu, Damon W.K. Wong
Artefacts Removal from Optical Coherence Tomography Angiography
Jui-Kai Wang, Randy H. Kardon, Mona K. Garvin
Automated Bruch’s Membrane Opening Segmentation in Cases of Optic Disc Swelling in Combined 2D and 3D SD-OCT Images Using Shape-Prior and Texture Information
Lama Seoud, Jihed Chelbi, Farida Cheriet
Automatic Grading of Diabetic Retinopathy on a Public Database
Malavika Bhaskaranand, Jorge Cuadros, Chaithanya Ramachandra, Sandeep Bhat, Muneeswar G. Nittala, Srinivas R. Sadda, Kaushal Solanki
EyeArt + EyePACS: Automated Retinal Image Analysis For Diabetic Retinopathy Screening in a Telemedicine System