Abstract
Recently there has been a great deal of interest in algorithms for constructing low-dimensional feature-space embeddings of high dimensional data sets in order to visualize inter- and intra-class relationships. In this paper we present a novel application of graph embedding in improving the accuracy of supervised classification schemes, especially in cases where object class labels cannot be reliably ascertained. By refining the initial training set of class labels we seek to improve the prior class distributions and thus classification accuracy. We also present a novel way of visualizing the class embeddings which makes it easy to appreciate inter-class relationships and to infer the presence of new classes which were not part of the original classification. We demonstrate the utility of the method in detecting prostatic adenocarcinoma from high-resolution MRI.
Chapter PDF
Similar content being viewed by others
Keywords
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
References
Dhillon, I., Modha, D., Spangler, W.: Class Visualization of high-dimensional data with applications. Computational Statistics & Data Analysis 41, 59–90 (2002)
Iwata, T., Saito, K., et al.: Parametric Embedding for Class Visualization. In: NIPS (2004)
Globerson, A., Chechik, G.: Euclidean Embedding of Co-occurrence Data. NIPS (2004)
Zhong, H., Shi, J., Visontai, M.: Detecting Unusual Activity in Video. CVPR (2004)
Madabhushi, A., Feldman, M., Metaxas, D., Tomaszeweski, J., Chute, D.: Automated Detection of Prostatic Adenocarcinoma from High Resolution in vitro prostate MR studies. IEEE Trans. Med. Imag. (accepted)
Duda, R., Hart, P.: Pattern Classification and Scene Analysis. Wiley, New York (1973)
Joliffe, T.: Principal Component Analysis. Springer, Heidelberg (1986)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Madabhushi, A., Shi, J., Rosen, M., Tomaszeweski, J.E., Feldman, M.D. (2005). Graph Embedding to Improve Supervised Classification and Novel Class Detection: Application to Prostate Cancer. In: Duncan, J.S., Gerig, G. (eds) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2005. MICCAI 2005. Lecture Notes in Computer Science, vol 3749. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11566465_90
Download citation
DOI: https://doi.org/10.1007/11566465_90
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-29327-9
Online ISBN: 978-3-540-32094-4
eBook Packages: Computer ScienceComputer Science (R0)