Semi Supervised Multi Kernel (SeSMiK) Graph Embedding: Identifying Aggressive Prostate Cancer via Magnetic Resonance Imaging and Spectroscopy
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 (SeSMiKGE). 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 fromindolent low-grade by integrating information from1.5 Tesla in vivo Magnetic Resonance Imaging (anatomic) and Spectroscopy (metabolic). Comparing SeSMiK-GEwith 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.
Pallavi Tiwari John Kurhanewicz Mark Rosen Anant Madabhushi
Department of Biomedical Engineering, Rutgers University, USA Department of Radiology, University of California, San Francisco, USA Department of Radiology, University of Pennsylvania, Philadelphia, USA
国际会议
北京
英文
666–673
2010-09-01（万方平台首次上网日期，不代表论文的发表时间）