A novel content-based medical image retrieval method with metric learning via rank correlation is proposed in this paper. A new rank correlation measure is proposed to learn a metric encoding the pairwise similarity between images via direct optimization. Our method has been evaluated with a large population-based dataset composed of 5000 slit-lamp images with different nuclear cataract severities. Experimental results and statistical analysis demonstrate the superiority of our method over several popular metric learning methods in content-based slit-lamp image retrieval. © 2010 Springer-Verlag Berlin Heidelberg.
CITATION STYLE
Huang, W., Chan, K. L., Li, H., Lim, J. H., Liu, J., & Wong, T. Y. (2010). Content-based medical image retrieval with metric learning via rank correlation. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 6357 LNCS, pp. 18–25). https://doi.org/10.1007/978-3-642-15948-0_3
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