Pallavi Tiwari1, John Kurhanewicz2,
Anant Madabhushi1
1Biomedical Engineering,
Rutgers University, Piscataway, NJ, United States; 2Department of
Radiology & Biomedical Imaging, University of California, San Francisco,
San Francisco, United States
In this work we present a novel multi-protocol MRI classifier, semi-supervised multi-kernel (SeSMiK), for quantitatively combining features from T2-w magnetic resonance (MR) imaging (T2