Name
Title | ||
|---|---|---|
Semi supervised multi kernel (SeSMiK) graph embedding: identifying aggressive prostate cancer via magnetic resonance imaging and spectroscopy. |
Abstract | ||
|---|---|---|
With the wide array of multi scale, multi-modal data now available for disease characterization, the major challenge in integrated disease diagnostics is to able to represent the different data streams in a common framework while overcoming differences in scale and dimensionality. This common knowledge representation framework is an important pre-requisite to develop integrated meta-classifiers for disease classification. In this paper, we present a unified data fusion framework, Semi Supervised Multi Kernel Graph Embedding (SeSMiK-GE). Our method allows for representation of individual data modalities via a combined multi-kernel framework followed by semi- supervised dimensionality reduction, where partial label information is incorporated to embed high dimensional data in a reduced space. In this work we evaluate SeSMiK-GE for distinguishing (a) benign from cancerous (CaP) areas, and (b) aggressive high-grade prostate cancer from indolent low-grade by integrating information from 1.5 Tesla in vivo Magnetic Resonance Imaging (anatomic) and Spectroscopy (metabolic). Comparing SeSMiK-GE with unimodal T2w, MRS classifiers and a previous published non-linear dimensionality reduction driven combination scheme (ScEPTre) yielded classification accuracies of (a) 91.3% (SeSMiK), 66.1% (MRI), 82.6% (MRS) and 86.8% (ScEPTre) for distinguishing benign from CaP regions, and (b) 87.5% (SeSMiK), 79.8% (MRI), 83.7% (MRS) and 83.9% (ScEPTre) for distinguishing high and low grade CaP over a total of 19 multi-modal MRI patient studies. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1007/978-3-642-15711-0_83 | MICCAI (3) |
Keywords | Field | DocType |
graph embedding,multi kernel,cap region,individual data modality,embed high dimensional data,multi-modal data,common knowledge representation framework,common framework,mrs classifier,aggressive prostate cancer,magnetic resonance imaging,combined multi-kernel framework,different data stream,unified data fusion framework,data fusion,magnetic resonance image,common knowledge,high dimensional data | Data stream mining,Dimensionality reduction,Computer science,Prostate cancer,Artificial intelligence,Computer vision,Clustering high-dimensional data,Pattern recognition,Graph embedding,Sensor fusion,Curse of dimensionality,Machine learning,Magnetic resonance imaging | Conference |
Volume | Issue | ISSN |
13 | Pt 3 | 0302-9743 |
ISBN | Citations | PageRank |
3-642-15710-6 | 14 | 0.95 |
References | Authors | |
8 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Pallavi Tiwari | 1 | 119 | 14.87 |
| John Kurhanewicz | 2 | 81 | 8.45 |
| Mark Rosen | 3 | 14 | 0.95 |
| Anant Madabhushi | 4 | 1736 | 139.21 |