Name
Title | ||
|---|---|---|
Spectral embedding based probabilistic boosting tree (ScEPTre): classifying high dimensional heterogeneous biomedical data. |
Abstract | ||
|---|---|---|
The major challenge with classifying high dimensional biomedical data is in identifying the appropriate feature representation to (a) overcome the curse of dimensionality, and (b) facilitate separation between the data classes. Another challenge is to integrate information from two disparate modalities, possibly existing in different dimensional spaces, for improved classification. In this paper, we present a novel data representation, integration and classification scheme, Spectral Embedding based Probabilistic boosting Tree (ScEPTre), which incorporates Spectral Embedding (SE) for data representation and integration and a Probabilistic Boosting Tree classifier for data classification. SE provides an alternate representation of the data by non-linearly transforming high dimensional data into a low dimensional embedding space such that the relative adjacencies between objects are preserved. We demonstrate the utility of ScEPTre to classify and integrate Magnetic Resonance (MR) Spectroscopy (MRS) and Imaging (MRI) data for prostate cancer detection. Area under the receiver operating Curve (AUC) obtained via randomized cross validation on 15 prostate MRI-MRS studies suggests that (a) ScEPTre on MRS significantly outperforms a Haar wavelets based classifier, (b) integration of MRI-MRS via ScEPTre performs significantly better compared to using MRI and MRS alone, and (c) data integration via ScEPTre yields superior classification results compared to combining decisions from individual classifiers (or modalities). |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1007/978-3-642-04271-3_102 | MICCAI |
Keywords | Field | DocType |
high dimensional biomedical data,sceptre yield,biomedical data,data classification,data class,data representation,novel data representation,appropriate feature representation,alternate representation,high dimensional data,probabilistic boosting tree,data integration,classifying high dimensional heterogeneous,curse of dimensionality,mr spectroscopy,magnetic resonance,data integrity,cross validation,receiver operator curve | Data integration,Data mining,Clustering high-dimensional data,External Data Representation,Pattern recognition,Computer science,Curse of dimensionality,Artificial intelligence,Boosting (machine learning),Probabilistic logic,Data classification,Classifier (linguistics) | Conference |
Volume | Issue | ISSN |
12 | Pt 2 | 0302-9743 |
Citations | PageRank | References |
7 | 0.70 | 8 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Pallavi Tiwari | 1 | 119 | 14.87 |
| Mark Rosen | 2 | 7 | 0.70 |
| Galen Reed | 3 | 31 | 2.59 |
| John Kurhanewicz | 4 | 81 | 8.45 |
| Anant Madabhushi | 5 | 1736 | 139.21 |