Connection | Profiles RNS

Search Result Details

Back to Search Results

This page shows the details of why an item matched the keywords from your search.

Search Results

Whitney, Heather

One or more keywords matched the following properties of Whitney, Heather

Property	Value
overview	Heather M. Whitney, PhD is a research assistant professor in the Department of Radiology at the University of Chicago. Dr. Whitney received a Master of Science in Medical Physics from the Vanderbilt University School of Medicine and Master of Science and PhD in Physics from Vanderbilt University. While at Vanderbilt, she trained and conducted research at the Vanderbilt University Institute of Imaging Science with John Gore as her advisor, and additionally collaborated with faculty in the Department of Radiation Oncology. Before coming to the University of Chicago, she was a tenured professor of physics at a small liberal arts college, where she fostered an NIH-funded research program in medical physics in collaboration with faculty in Radiology at the University of Chicago. At the University of Chicago, she conducts research in computer-aided diagnosis of breast and ovarian cancer, focusing on the modalities of dynamic contrast-enhanced magnetic resonance imaging and ultrasound. Her primary areas of interest are in artificial intelligence and radiomics across the imaging and classification pipeline, from image acquisition to performance evaluation and data harmonization. She also conducts research and collaborates in MIDRC, the Medical Imaging and Data Resource Center. Within MIDRC she works on methods of task-based distributions, interoperability between data enclaves, and monitoring and studying the diversity and representativeness of the MIDRC data commons to foster research in AI and health disparities.

Property

Value

overview

Heather M. Whitney, PhD is a research assistant professor in the Department of Radiology at the University of Chicago. Dr. Whitney received a Master of Science in Medical Physics from the Vanderbilt University School of Medicine and Master of Science and PhD in Physics from Vanderbilt University. While at Vanderbilt, she trained and conducted research at the Vanderbilt University Institute of Imaging Science with John Gore as her advisor, and additionally collaborated with faculty in the Department of Radiation Oncology. Before coming to the University of Chicago, she was a tenured professor of physics at a small liberal arts college, where she fostered an NIH-funded research program in medical physics in collaboration with faculty in Radiology at the University of Chicago. At the University of Chicago, she conducts research in computer-aided diagnosis of breast and ovarian cancer, focusing on the modalities of dynamic contrast-enhanced magnetic resonance imaging and ultrasound. Her primary areas of interest are in artificial intelligence and radiomics across the imaging and classification pipeline, from image acquisition to performance evaluation and data harmonization. She also conducts research and collaborates in MIDRC, the Medical Imaging and Data Resource Center. Within MIDRC she works on methods of task-based distributions, interoperability between data enclaves, and monitoring and studying the diversity and representativeness of the MIDRC data commons to foster research in AI and health disparities.

One or more keywords matched the following items that are connected to Whitney, Heather

Item Type	Name
Concept	Breast Neoplasms
Concept	Neoplasms
Academic Article	Additive Benefit of Radiomics Over Size Alone in the Distinction Between Benign Lesions and Luminal A Cancers on a Large Clinical Breast MRI Dataset.
Academic Article	A deep learning methodology for improved breast cancer diagnosis using multiparametric MRI.
Academic Article	Radiomics methodology for breast cancer diagnosis using multiparametric magnetic resonance imaging.
Academic Article	Improved Classification of Benign and Malignant Breast Lesions Using Deep Feature Maximum Intensity Projection MRI in Breast Cancer Diagnosis Using Dynamic Contrast-enhanced MRI.
Academic Article	Robustness of radiomic features of benign breast lesions and hormone receptor positive/HER2-negative cancers across DCE-MR magnet strengths.
Academic Article	Multi-Stage Harmonization for Robust AI across Breast MR Databases.
Academic Article	Effect of biopsy on the MRI radiomics classification of benign lesions and luminal A cancers.
Academic Article	Differences in Molecular Subtype Reference Standards Impact AI-based Breast Cancer Classification with Dynamic Contrast-enhanced MRI.
Grant	Assessment of Repeatability and Robustness of Radiomics in Breast Cancer Imaging
Academic Article	Role of sureness in evaluating AI/CADx: Lesion-based repeatability of machine learning classification performance on breast MRI.
Academic Article	Special Section Guest Editorial: Global Health, Bias, and Diversity in AI in Medical Imaging.

Search Criteria

health
disparities
cancer