activity
20162019
most citedMultiparametric Deep Learning and Radiomics for Tumor Grading and Treatment Response Assessment of Brain Cancer: Preliminary Results

5 citations · 5 across the 2 of their papers we have counts for

collaborators
Showing 2018Show all

5 papers · 1 filter

physics.med-ph2018

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…

cs.CV2018

Radiomic Synthesis Using Deep Convolutional Neural Networks

Vishwa S. Parekh, Michael A. Jacobs

Radiomics is a rapidly growing field that deals with modeling the textural information present in the different tissues of interest for clinical decision support. However, the proc…

cs.CV2018

MPRAD: A Multiparametric Radiomics Framework

Vishwa S. Parekh, Michael A. Jacobs

Multiparametric radiological imaging is vital for detection, characterization and diagnosis of many different diseases. The use of radiomics for quantitative extraction of textural…

cs.LG2018

DreamNLP: Novel NLP System for Clinical Report Metadata Extraction using Count Sketch Data Streaming Algorithm: Preliminary Results

Sanghyun Choi, Nikita Ivkin, Vladimir Braverman +1

Extracting information from electronic health records (EHR) is a challenging task since it requires prior knowledge of the reports and some natural language processing algorithm (N…

physics.med-ph2018

Multiparametric Deep Learning Tissue Signatures for a Radiological Biomarker of Breast Cancer: Preliminary Results

Vishwa S. Parekh, Katarzyna J. Macura, Susan Harvey +4

A new paradigm is beginning to emerge in Radiology with the advent of increased computational capabilities and algorithms. This has led to the ability of real time learning by comp…