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Connection

Co-Authors

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

12.081
  1. Introduction to the JMI Special Issue on Advances in Breast Imaging. J Med Imaging (Bellingham). 2025 Nov; 12(Suppl 2):S22001.
    View in: PubMed
    Score: 0.961
  2. Sureness of classification of breast cancers as pure ductal carcinoma in situ or with invasive components on dynamic contrast-enhanced magnetic resonance imaging: application of likelihood assurance metrics for computer-aided diagnosis. J Med Imaging (Bellingham). 2025 Nov; 12(Suppl 2):S22012.
    View in: PubMed
    Score: 0.946
  3. AI analysis of medical images at scale as a health disparities probe: a feasibility demonstration using chest radiographs. ArXiv. 2025 Apr 08.
    View in: PubMed
    Score: 0.933
  4. AI-based automated segmentation for ovarian/adnexal masses and their internal components on ultrasound imaging. J Med Imaging (Bellingham). 2024 Jul; 11(4):044505.
    View in: PubMed
    Score: 0.891
  5. 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: 0.833
  6. 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: 0.828
  7. 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: 0.766
  8. Multi-Stage Harmonization for Robust AI across Breast MR Databases. Cancers (Basel). 2021 Sep 26; 13(19).
    View in: PubMed
    Score: 0.730
  9. 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: 0.718
  10. 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: 0.656
  11. 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: 0.643
  12. 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: 0.610
  13. 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: 0.578
  14. Demonstration of Interoperability Between MIDRC and N3C: A COVID-19 Severity Prediction Use Case. J Imaging Inform Med. 2025 Aug 14.
    View in: PubMed
    Score: 0.239
  15. Multimodal data curation via interoperability: use cases with the Medical Imaging and Data Resource Center. Sci Data. 2025 Aug 01; 12(1):1340.
    View in: PubMed
    Score: 0.238
  16. 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: 0.212
  17. 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: 0.208
  18. 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: 0.192
  19. 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: 0.175
  20. Radiomics methodology for breast cancer diagnosis using multiparametric magnetic resonance imaging. J Med Imaging (Bellingham). 2020 Jul; 7(4):044502.
    View in: PubMed
    Score: 0.169
  21. A deep learning methodology for improved breast cancer diagnosis using multiparametric MRI. Sci Rep. 2020 06 29; 10(1):10536.
    View in: PubMed
    Score: 0.168
  22. Hybrid artificial intelligence echogenic components-based diagnosis of adnexal masses on ultrasound. Med Phys. 2025 Jul; 52(7):e17983.
    View in: PubMed
    Score: 0.059
  23. MIDRC mRALE Mastermind Grand Challenge: AI to predict COVID severity on chest radiographs. J Med Imaging (Bellingham). 2025 Mar; 12(2):024505.
    View in: PubMed
    Score: 0.058
  24. Hybrid artificial intelligence echogenic components-based diagnosis of adnexal masses on ultrasound. ArXiv. 2025 Apr 16.
    View in: PubMed
    Score: 0.058
  25. Impact of retraining and data partitions on the generalizability of a deep learning model in the task of COVID-19 classification on chest radiographs. J Med Imaging (Bellingham). 2024 Nov; 11(6):064503.
    View in: PubMed
    Score: 0.057
  26. 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: 0.054
  27. 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: 0.051
  28. 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: 0.050
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.