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Connection

Co-Authors

This is a "connection" page, showing publications co-authored by Maryellen L. Giger and Heather Whitney.
Connection Strength

8.662
  1. 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.
    View in: PubMed
    Score: 0.945
  2. 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.
    View in: PubMed
    Score: 0.939
  3. 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.
    View in: PubMed
    Score: 0.868
  4. Multi-Stage Harmonization for Robust AI across Breast MR Databases. Cancers (Basel). 2021 Sep 26; 13(19).
    View in: PubMed
    Score: 0.828
  5. 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.
    View in: PubMed
    Score: 0.814
  6. Harmonization of radiomic features of breast lesions across international DCE-MRI datasets. J Med Imaging (Bellingham). 2020 Jan; 7(1):012707.
    View in: PubMed
    Score: 0.743
  7. 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.
    View in: PubMed
    Score: 0.729
  8. Effect of biopsy on the MRI radiomics classification of benign lesions and luminal A cancers. J Med Imaging (Bellingham). 2019 Jul; 6(3):031408.
    View in: PubMed
    Score: 0.691
  9. 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.
    View in: PubMed
    Score: 0.655
  10. 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.
    View in: PubMed
    Score: 0.240
  11. Predicting intensive care need for COVID-19 patients using deep learning on chest radiography. J Med Imaging (Bellingham). 2023 Jul; 10(4):044504.
    View in: PubMed
    Score: 0.236
  12. Impact of continuous learning on diagnostic breast MRI AI: evaluation on an independent clinical dataset. J Med Imaging (Bellingham). 2022 May; 9(3):034502.
    View in: PubMed
    Score: 0.217
  13. 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.
    View in: PubMed
    Score: 0.199
  14. Radiomics methodology for breast cancer diagnosis using multiparametric magnetic resonance imaging. J Med Imaging (Bellingham). 2020 Jul; 7(4):044502.
    View in: PubMed
    Score: 0.192
  15. A deep learning methodology for improved breast cancer diagnosis using multiparametric MRI. Sci Rep. 2020 06 29; 10(1):10536.
    View in: PubMed
    Score: 0.190
  16. 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.
    View in: PubMed
    Score: 0.062
  17. 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.
    View in: PubMed
    Score: 0.058
  18. Differences in Molecular Subtype Reference Standards Impact AI-based Breast Cancer Classification with Dynamic Contrast-enhanced MRI. Radiology. 2023 04; 307(1):e220984.
    View in: PubMed
    Score: 0.057
Connection Strength

The connection strength for concepts is the sum of the scores for each matching publication.

Publication scores are based on many factors, including how long ago they were written and whether the person is a first or senior author.