Area of research
Radiology, Nuclear Medicine and Imaging · Cognitive Neuroscience
Research interest
Research interests include Radiomics and Machine Learning in Medical Imaging, Functional Brain Connectivity Studies, Advanced MRI Techniques and Applications, and Medical Imaging Techniques and Applications.
Deep Learning Modeling to Differentiate Multiple Sclerosis From MOG Antibody–Associated Disease
MR Intensity Normalization Methods Impact Sequence Specific Radiomics Prognostic Model Performance in Primary and Recurrent High-Grade Glioma
MR-Class: A Python Tool for Brain MR Image Classification Utilizing One-vs-All DCNNs to Deal with the Open-Set Recognition Problem
MR Intensity Normalization Methods Impact Sequence Specific Radiomics Prognostic Model Performance in Primary and Recurrent High-Grade Glioma
MR-Class: A Python Tool for Brain Mr Image Classification Utilizing One-Vs-All DCNNS to Deal With the Open-Set Recognition Problem
Deep Learning–based Automatic Lung Segmentation on Multiresolution CT Scans from Healthy and Fibrotic Lungs in Mice
Improved risk stratification via integration of radiomics and dosiomics features in patients with recurrent high-grade glioma undergoing carbon ion radiotherapy (CIRT).
PD-L1-R: A MR based surrogate for PD-L1 expression in Glioblastoma multiforme.
pyCuRT: An Automated Data Curation Workflow for Radiotherapy Big Data Analysis using Pythons’ NyPipe
Genetic (<i>APOE</i>, <i>BDNF</i>) influences on functional language network connectivity in healthy older adults