IEEE Transactions on Biomedical Engineering

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Robust Collaborative Clustering of Subjects and Radiomic Features for Cancer Prognosis
A robust collaborative clustering method has been developed in a Bayesian framework to simultaneously cluster patients and imaging features into distinct groups respectively, aiming to learn a compact set of discriminative features in radiomics studies. Experiments on synthetic data have demonstrated the effectiveness of the proposed approach in data clustering, and evaluation results on an FDG-PET/CT dataset of rectal cancer patients have demonstrated that the proposed method outperforms alternative methods in terms of both patient stratification and prediction of patient clinical outcomes... Read more
Featured Articles
Unsupervised Spatiotemporal Analysis of FMRI Data Using Graph-Based Visualizations of Self-Organizing Map
Santosh B. Katwal, John C. Gore, René Marois, and Baxter P. Rogers Functional magnetic resonance imaging (fMRI) data are commonly analyzed voxel-by-voxel using linear regression models (statistical parametric mapping) which requires information about stimulus timing and assumptions about the shape and... Read more