A computer-aided diagnosis system of nuclear cataract via ranking.
A novel computer-aided diagnosis system of nuclear cataract via ranking is firstly proposed in this paper. The grade of nuclear cataract in a slit-lamp image is predicted based on its neighboring labeled images in a ranked images list, which is achieved using an optimal ranking function. A new ranking evaluation measure is proposed for learning the optimal ranking function via direct optimization. Our system has been tested by a large dataset composed of 1000 slit-lamp images from 1000 different cases. Both experimental results and comparison with several state-of-the-art methods indicate the superiority of our system.
Huang
Wei
W
School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore.
Li
Huiqi
H
Chan
Kap Luk
KL
Lim
Joo Hwee
JH
Liu
Jiang
J
Wong
Tien Yin
TY
D000465:Algorithms; D001185:Artificial Intelligence; D002386:Cataract / Q000473:pathology*; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D009887:Ophthalmoscopy / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity