unsupervised learning

Robust Collaborative Clustering of Subjects and Radiomic Features for Cancer Prognosis

Author(s): Hangfan Liu, Hongming Li, Mohamad Habes, Yuemeng Li, Pamela Boimel, James Janopaul-Naylor, Ying Xiao, Edgar Ben-Josef, Yong Fan
Robust Collaborative Clustering of Subjects and Radiomic Features for Cancer Prognosis 721 294 IEEE Transactions on Biomedical Engineering (TBME)
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

Unsupervised Spatiotemporal Analysis of FMRI Data Using Graph-Based Visualizations of Self-Organizing Map

Unsupervised Spatiotemporal Analysis of FMRI Data Using Graph-Based Visualizations of Self-Organizing Map 150 150 IEEE Transactions on Biomedical Engineering (TBME)

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…

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