Effect of biopsy on the MRI radiomics classification of benign lesions and luminal A cancers. - Heather M. Whitney; Karen Drukker; Alexandra Edwards; John Papaioannou; Maryellen L. Giger (IWBI, 2018)

Robustness of radiomic breast features of benign lesions and luminal A cancers across MR magnet strengths. - Heather M. Whitney; Karen Drukker; Alexandra Edwards; John Papaioannou; Maryellen L. Giger (Computer-Aided Diagnosis, 2018)

Radiomics and deep learning of diffusion-weighted MRI in the diagnosis of breast cancer. - Qiyuan Hu; Heather M. Whitney; Alexandra Edwards; John Papaioannou; Maryellen L. Giger (Computer-Aided Diagnosis, 2019)

Effect of diversity of patient population and acquisition systems on the use of radiomics and machine learning for classification of 2, 397 breast lesions. - Heather M. Whitney; Yu Ji; Hui Li 024; Alexandra Edwards; John Papaioannou; Peifang Liu; Maryellen L. Giger (Computer-Aided Diagnosis, 2019)

Transfer Learning in 4D for Breast Cancer Diagnosis using Dynamic Contrast-Enhanced Magnetic Resonance Imaging. - Qiyuan Hu; Heather M. Whitney; Maryellen L. Giger (CoRR, 2019)

Using ResNet feature extraction in computer-aided diagnosis of breast cancer on 927 lesions imaged with multiparametric MRI. - Qiyuan Hu; Heather M. Whitney; Maryellen L. Giger (Computer-Aided Diagnosis, 2020)

Case-based repeatability of machine learning classification performance on breast MRI. - Michael Vieceli; Amy Van Dusen; Karen Drukker; Hiroyuki Abe; Maryellen L. Giger; Heather M. Whitney (Computer-Aided Diagnosis, 2020)

Improvement of classification performance using harmonization across field strength of radiomic features extracted from DCE-MR images of the breast. - Heather M. Whitney; Maryellen L. Giger (Computer-Aided Diagnosis, 2020)

Repeatability profiles towards consistent sensitivity and specificity levels for machine learning on breast DCE-MRI. - Amy Van Dusen; Michael Vieceli; Karen Drukker; Hiroyuki Abe; Maryellen L. Giger; Heather M. Whitney (Image Perception, Observer Performance, and Technology Assessment, 2020)

Comparison of Breast MRI Tumor Classification Using Human-Engineered Radiomics, Transfer Learning From Deep Convolutional Neural Networks, and Fusion Method. - Heather M. Whitney; Hui Li 024; Yu Ji; Peifang Liu; Maryellen L. Giger (Proc. IEEE, 2020)

Case-based diagnostic classification repeatability using radiomic features extracted from full-field digital mammography images of breast lesions. - Paul Amstutz; Karen Drukker; Hui Li 024; Hiroyuki Abe; Maryellen L. Giger; Heather M. Whitney (Computer-Aided Diagnosis, 2021)

Comparison of diagnostic performances, case-based repeatability, and operating sensitivity and specificity in classification of breast lesions using DCE-MRI. - Michelle de Oliveira; Karen Drukker; Michael Vieceli; Hiroyuki Abe; Maryellen L. Giger; Heather M. Whitney (Image Perception, Observer Performance, and Technology Assessment, 2021)

Effect of different molecular subtype reference standards in AI training: implications for DCE-MRI radiomics of breast cancers. - Heather M. Whitney; Yu Ji; Hui Li 024; Peifang Liu; Maryellen L. Giger (Computer-Aided Diagnosis, 2022)

Sequestration of imaging studies in MIDRC: a multi-institutional data commons. - Natalie M. Baughan; Heather M. Whitney; Karen Drukker; Berkman Sahiner; Tingting Hu; Hyun J. Grace Kim; Michael F. McNitt-Gray; Kyle J. Myers; Maryellen L. Giger (Image Perception, Observer Performance, and Technology Assessment, 2022)

Case-based repeatability and operating point variability of AI: breast lesion classification based on deep transfer learning. - Heather M. Whitney; Karen Drukker; Hiroyuki Abe; Maryellen L. Giger (Image Perception, Observer Performance, and Technology Assessment, 2022)

Assistance tools for the evaluation of machine learning algorithm performance: the decision tree-based tools developed by the Medical Imaging and Data Resource Center (MIDRC) Technology Development Project (TDP) 3c effort. - Karen Drukker; Berkman Sahiner; Tingting Hu; Hyun J. Grace Kim; Heather M. Whitney; Natalie M. Baughan; Kyle J. Myers; Maryellen L. Giger; Michael F. McNitt-Gray (Image Perception, Observer Performance, and Technology Assessment, 2023)

Investigation of demographic implicit discrimination and disparate impact in chest radiography image-based AI for COVID-19 severity prediction. - Heather M. Whitney; Hui Li 024; Karen Drukker; Michael Reeve; Maryellen L. Giger (Image Perception, Observer Performance, and Technology Assessment, 2023)

AI analysis of medical images at scale as a health disparities probe: a feasibility demonstration using chest radiographs. - Heather M. Whitney; Hui Li 024; Karen Drukker; Elbert Huang; Maryellen L. Giger (CoRR, 2025)