5 citations · 5 across the 2 of their papers we have counts for
5 papers
Multiparametric Deep Learning Tissue Signatures for Muscular Dystrophy: Preliminary Results
Alex E. Bocchieri, Vishwa S. Parekh, Kathryn R. Wagner. Shivani Ahlawat +3
A current clinical challenge is identifying limb girdle muscular dystrophy 2I(LGMD2I)tissue changes in the thighs, in particular, separating fat, fat-infiltrated muscle, and muscle…
Multiparametric Deep Learning and Radiomics for Tumor Grading and Treatment Response Assessment of Brain Cancer: Preliminary Results
Vishwa S. Parekh, John Laterra, Chetan Bettegowda +3
Radiomics is an exciting new area of texture research for extracting quantitative and morphological characteristics of pathological tissue. However, to date, only single images hav…
Tumor Connectomics: Mapping the intra-tumoral complex interaction network
Vishwa S. Parekh, Michael A. Jacobs
Tumors are extremely heterogeneous and comprise of a number of intratumor microenvironments or sub-regions. These tumor microenvironments may interact with eac based on complex hig…
Advanced machine learning informatics modeling using clinical and radiological imaging metrics for characterizing breast tumor characteristics with the OncotypeDX gene array
Michael A. Jacobs, Christopher Umbricht, Vishwa Parekh +5
Purpose-Optimal use of established and imaging methods, such as multiparametric magnetic resonance imaging(mpMRI) can simultaneously identify key functional parameters and provide…
Unsupervised Non Linear Dimensionality Reduction Machine Learning methods applied to Multiparametric MRI in cerebral ischemia: Preliminary Results
Vishwa S. Parekh, Jeremy R. Jacobs, Michael A. Jacobs
The evaluation and treatment of acute cerebral ischemia requires a technique that can determine the total area of tissue at risk for infarction using diagnostic magnetic resonance…