Abstract
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.
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References
Greenspan, H., Deserno, T.: Content-based Image Retrieval for Biomedical Image Archives: Achievements, Problems, and Prospects. In: MICCAI 2007 Workshop (2007)
Müller, H., Michoux, N., Bandon, D., Geissbuhler, A.: A Review of Content-based Image Retrieval Systems in Medical Applications - Clinical Benefits and Future Directions. IJMI 73(1), 1–23 (2004)
World Health Organization: The World Health Report: Life in the 21st Century - A Vision for All. Geneva (1998)
Yang, L., Jin, R.: Distance Metric Learning: A Comprehensive Survey (2006)
Xing, E., Ng, A., Jordan, M., Russell, S.: Distance Metric Learning, with Application to Clustering with Side-information. In: NIPS, pp. 505–512 (2002)
Weinberger, K., Blitzer, J., Saul, L.: Distance Metric Learning for Large Margin Nearest Neighborhood Classification. In: NIPS, pp. 265–272 (2005)
Schultz, M., Joachims, T.: Learning a Distance Metric from Relative Comparisons. In: NIPS, pp. 41–48 (2004)
Gold, C., Sollich, P.: Model Selection for Support Vector Machine Classification. Neurocomputing 55, 221–249 (2003)
Weston, J.: Leave-one-out Support Vector Machines. In: IJCAI, pp. 727–733 (1999)
Chapelle, O., Vapnik, V., Bousquet, O., Mukherjee, S.: Choosing Multiple Parameters for Support Vector Machines. Machine Learning 46, 131–159 (2002)
Kendall, M.: A New Measure of Rank Correlation. Biometrika 30, 81–93 (1938)
Lee, J., Jin, R., Jain, A.: Rank-based Distance Metric Learning: An Application to Image Retrieval. In: CVPR, pp. 1–8 (2008)
Klein, B., Klein, R., Linton, K., Magli, Y., Neider, M.: Assessment of Cataracts from Photographs in the Beaver Dam Eye Study. Ophthalmology 97(11), 1428–1433 (1990)
Li, H., Lim, J.H., Liu, J., Mitchell, P., Tan, A., Wang, J., Wong, T.Y.: A Computer-aided Diagnosis System of Nuclear Cataract. IEEE TBME 57(7), 1690–1698 (2010)
Rice, J.: Mathematical Statistics and Data Analysis, 2nd edn (2007)
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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: Wang, F., Yan, P., Suzuki, K., Shen, D. (eds) Machine Learning in Medical Imaging. MLMI 2010. Lecture Notes in Computer Science, vol 6357. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15948-0_3
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DOI: https://doi.org/10.1007/978-3-642-15948-0_3
Publisher Name: Springer, Berlin, Heidelberg
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