Detecting unusual activity in video
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Class visualization of high-dimensional data with applications
Computational Statistics & Data Analysis
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
ISBRA'07 Proceedings of the 3rd international conference on Bioinformatics research and applications
MICCAI'07 Proceedings of the 10th international conference on Medical image computing and computer-assisted intervention
Computer-aided prognosis: predicting patient and disease outcome via multi-modal image analysis
ISBI'10 Proceedings of the 2010 IEEE international conference on Biomedical imaging: from nano to Macro
Prostate cancer segmentation using multispectral random walks
MICCAI'10 Proceedings of the 2010 international conference on Prostate cancer imaging: computer-aided diagnosis, prognosis, and intervention
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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.