IR evaluation methods for retrieving highly relevant documents
SIGIR '00 Proceedings of the 23rd annual international ACM SIGIR conference on Research and development in information retrieval
Optimizing search engines using clickthrough data
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
An efficient boosting algorithm for combining preferences
The Journal of Machine Learning Research
AdaRank: a boosting algorithm for information retrieval
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Focal biologically inspired feature for glaucoma type classification
MICCAI'11 Proceedings of the 14th international conference on Medical image computing and computer-assisted intervention - Volume Part III
Automatic grading of cortical and PSC cataracts using retroillumination lens images
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part II
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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.