154 citations · 156 across the 4 of their papers we have counts for
4 papers
Radiomic Deformation and Textural Heterogeneity (R-DepTH) Descriptor to characterize Tumor Field Effect: Application to Survival Prediction in Glioblastoma
Marwa Ismail, Prateek Prasanna, Kaustav Bera +10
The concept of tumor field effect implies that cancer is a systemic disease with its impact way beyond the visible tumor confines. For instance, in Glioblastoma (GBM), an aggressiv…
Radiomic features of multi-parametric MRI present stable associations with analogous histological features in brain cancer patients
Samuel Bobholz, Allison Lowman, Alexander Barrington +9
MR-derived radiomic features have demonstrated substantial predictive utility in modeling different prognostic factors of glioblastomas and other brain cancers. However, the biolog…
Build-A-FLAIR: Synthetic T2-FLAIR Contrast Generation through Physics Informed Deep Learning
Andrew S. Nencka, Andrew Klein, Kevin M. Koch +7
Purpose: Magnetic resonance imaging (MRI) exams include multiple series with varying contrast and redundant information. For instance, T2-FLAIR contrast is based upon tissue T2 dec…
Iterative annotation to ease neural network training: Specialized machine learning in medical image analysis
Brendon Lutnick, Brandon Ginley, Darshana Govind +7
Neural networks promise to bring robust, quantitative analysis to medical fields, but adoption is limited by the technicalities of training these networks. To address this translat…