Nastaran Emaminejad is a PhD candidate in Medical Physics at University of California at Los Angeles under supervision of Dr. Michael Mc-Nitt Gray and Dr. Matthew Brown. Her research interest focuses on quantitative medical imaging. She has been involved in developing and working with common computer aided diagnosis tools by using of machine learning and computer vision in providing precision medicine.
A novel physics-based data augmentation approach for improved robust deep learning in medical imaging: lung nodule CAD false positive reduction in CT low-dose environments
The effects of variations in parameters and algorithm choices on calculated radiomics feature values: initial investigations and comparisons to feature variability across CT image acquisition conditions
Towards quantitative imaging: stability of fully automated nodule segmentation across varied dose levels and reconstruction parameters in a low-dose CT screening patient cohort
The effects of slice thickness and radiation dose level variations on computer-aided diagnosis (CAD) nodule detection performance in pediatric chest CT scans
Evaluation of correlation between CT image features and ERCC1 protein expression in assessing lung cancer prognosis