Area of research
Radiology, Nuclear Medicine and Imaging · Artificial Intelligence
Research interest
Research interests include Radiomics and Machine Learning in Medical Imaging, Advanced MRI Techniques and Applications, MRI in cancer diagnosis, and AI in cancer detection.
Ethical framework for responsible foundational models in medical imaging
High‐resolution simulation of B<sub>0</sub> field conditions in the human heart from segmented computed tomography images
Distortion Removal and Deblurring of Single-Shot DWI MRI Scans
Integrating Eye Tracking and Speech Recognition Accurately Annotates MR Brain Images for Deep Learning: Proof of Principle
Segmentation of Brain Tumors Using DeepLabv3+
Eye Tracking for Deep Learning Segmentation Using Convolutional Neural Networks
Quantitative imaging biomarkers alliance (QIBA) recommendations for improved precision of DWI and DCE‐MRI derived biomarkers in multicenter oncology trials
Deep semantic lung segmentation for tracking potential pulmonary perfusion biomarkers in chronic obstructive pulmonary disease (COPD): The multi‐ethnic study of atherosclerosis COPD study
Quantitative imaging biomarkers alliance (QIBA) recommendations for improved precision of DWI and DCE‐MRI derived biomarkers in multicenter oncology trials
Prior to Initiation of Chemotherapy, Can We Predict Breast Tumor Response? Deep Learning Convolutional Neural Networks Approach Using a Breast MRI Tumor Dataset
Convolutional Neural Networks for the Detection and Measurement of Cerebral Aneurysms on Magnetic Resonance Angiography
Axillary Lymph Node Evaluation Utilizing Convolutional Neural Networks Using MRI Dataset
Convolutional Neural Network Based Breast Cancer Risk Stratification Using a Mammographic Dataset
Fusion of aerial lidar and images for road segmentation with deep CNN