Co-Authors
This is a "connection" page, showing publications co-authored by Maryellen L. Giger and Heather Whitney.
Connection Strength
8.748
-
Role of sureness in evaluating AI/CADx: Lesion-based repeatability of machine learning classification performance on breast MRI. Med Phys. 2024 Mar; 51(3):1812-1821.
Score: 0.954
-
Longitudinal assessment of demographic representativeness in the Medical Imaging and Data Resource Center open data commons. J Med Imaging (Bellingham). 2023 Nov; 10(6):61105.
Score: 0.948
-
Performance metric curve analysis framework to assess impact of the decision variable threshold, disease prevalence, and dataset variability in two-class classification. J Med Imaging (Bellingham). 2022 May; 9(3):035502.
Score: 0.877
-
Multi-Stage Harmonization for Robust AI across Breast MR Databases. Cancers (Basel). 2021 Sep 26; 13(19).
Score: 0.836
-
Robustness of radiomic features of benign breast lesions and hormone receptor positive/HER2-negative cancers across DCE-MR magnet strengths. Magn Reson Imaging. 2021 10; 82:111-121.
Score: 0.822
-
Harmonization of radiomic features of breast lesions across international DCE-MRI datasets. J Med Imaging (Bellingham). 2020 Jan; 7(1):012707.
Score: 0.751
-
Comparison of Breast MRI Tumor Classification Using Human-Engineered Radiomics, Transfer Learning From Deep Convolutional Neural Networks, and Fusion Methods. Proc IEEE Inst Electr Electron Eng. 2020 Jan; 108(1):163-177.
Score: 0.736
-
Effect of biopsy on the MRI radiomics classification of benign lesions and luminal A cancers. J Med Imaging (Bellingham). 2019 Jul; 6(3):031408.
Score: 0.698
-
Additive Benefit of Radiomics Over Size Alone in the Distinction Between Benign Lesions and Luminal A Cancers on a Large Clinical Breast MRI Dataset. Acad Radiol. 2019 02; 26(2):202-209.
Score: 0.662
-
Sequestration of imaging studies in MIDRC: stratified sampling to balance demographic characteristics of patients in a multi-institutional data commons. J Med Imaging (Bellingham). 2023 Nov; 10(6):064501.
Score: 0.243
-
Predicting intensive care need for COVID-19 patients using deep learning on chest radiography. J Med Imaging (Bellingham). 2023 Jul; 10(4):044504.
Score: 0.239
-
Impact of continuous learning on diagnostic breast MRI AI: evaluation on an independent clinical dataset. J Med Imaging (Bellingham). 2022 May; 9(3):034502.
Score: 0.219
-
Improved Classification of Benign and Malignant Breast Lesions Using Deep Feature Maximum Intensity Projection MRI in Breast Cancer Diagnosis Using Dynamic Contrast-enhanced MRI. Radiol Artif Intell. 2021 May; 3(3):e200159.
Score: 0.201
-
Radiomics methodology for breast cancer diagnosis using multiparametric magnetic resonance imaging. J Med Imaging (Bellingham). 2020 Jul; 7(4):044502.
Score: 0.194
-
A deep learning methodology for improved breast cancer diagnosis using multiparametric MRI. Sci Rep. 2020 06 29; 10(1):10536.
Score: 0.192
-
MIDRC-MetricTree: a decision tree-based tool for recommending performance metrics in artificial intelligence-assisted medical image analysis. J Med Imaging (Bellingham). 2024 Mar; 11(2):024504.
Score: 0.062
-
Toward fairness in artificial intelligence for medical image analysis: identification and mitigation of potential biases in the roadmap from data collection to model deployment. J Med Imaging (Bellingham). 2023 Nov; 10(6):061104.
Score: 0.058
-
Differences in Molecular Subtype Reference Standards Impact AI-based Breast Cancer Classification with Dynamic Contrast-enhanced MRI. Radiology. 2023 04; 307(1):e220984.
Score: 0.057