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Connection

Co-Authors

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

8.973
  1. Breast MRI radiomics for the pretreatment prediction of response to neoadjuvant chemotherapy in node-positive breast cancer patients. J Med Imaging (Bellingham). 2019 Jul; 6(3):034502.
    View in: PubMed
    Score: 0.721
  2. Most-enhancing tumor volume by MRI radiomics predicts recurrence-free survival "early on" in neoadjuvant treatment of breast cancer. Cancer Imaging. 2018 Apr 13; 18(1):12.
    View in: PubMed
    Score: 0.652
  3. Computerized detection of breast cancer on automated breast ultrasound imaging of women with dense breasts. Med Phys. 2014 Jan; 41(1):012901.
    View in: PubMed
    Score: 0.484
  4. Interreader scoring variability in an observer study using dual-modality imaging for breast cancer detection in women with dense breasts. Acad Radiol. 2013 Jul; 20(7):847-53.
    View in: PubMed
    Score: 0.461
  5. Repeatability in computer-aided diagnosis: application to breast cancer diagnosis on sonography. Med Phys. 2010 Jun; 37(6):2659-69.
    View in: PubMed
    Score: 0.378
  6. Repeatability in computer-aided diagnosis: Application to breast cancer diagnosis on sonography. Med Phys. 2010 Jun; 37(6Part1):2659-2669.
    View in: PubMed
    Score: 0.378
  7. Automated method for improving system performance of computer-aided diagnosis in breast ultrasound. IEEE Trans Med Imaging. 2009 Jan; 28(1):122-8.
    View in: PubMed
    Score: 0.343
  8. Breast US computer-aided diagnosis workstation: performance with a large clinical diagnostic population. Radiology. 2008 Aug; 248(2):392-7.
    View in: PubMed
    Score: 0.330
  9. Multimodality computerized diagnosis of breast lesions using mammography and sonography. Acad Radiol. 2005 Aug; 12(8):970-9.
    View in: PubMed
    Score: 0.270
  10. 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: 0.247
  11. U-Net breast lesion segmentations for breast dynamic contrast-enhanced magnetic resonance imaging. J Med Imaging (Bellingham). 2023 Nov; 10(6):064502.
    View in: PubMed
    Score: 0.240
  12. 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
  13. 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: 0.236
  14. 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
  15. 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: 0.235
  16. 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: 0.231
  17. 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: 0.217
  18. Role of standard and soft tissue chest radiography images in deep-learning-based early diagnosis of COVID-19. J Med Imaging (Bellingham). 2021 Jan; 8(Suppl 1):014503.
    View in: PubMed
    Score: 0.207
  19. 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: 0.203
  20. 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: 0.173
  21. Combined Benefit of Quantitative Three-Compartment Breast Image Analysis and Mammography Radiomics in the Classification of Breast Masses in a Clinical Data Set. Radiology. 2019 03; 290(3):621-628.
    View in: PubMed
    Score: 0.171
  22. Deep learning in medical imaging and radiation therapy. Med Phys. 2019 Jan; 46(1):e1-e36.
    View in: PubMed
    Score: 0.170
  23. 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: 0.164
  24. Fuzzy c-means segmentation of major vessels in angiographic images of stroke. J Med Imaging (Bellingham). 2018 Jan; 5(1):014501.
    View in: PubMed
    Score: 0.160
  25. Quantitative MRI radiomics in the prediction of molecular classifications of breast cancer subtypes in the TCGA/TCIA data set. NPJ Breast Cancer. 2016; 2.
    View in: PubMed
    Score: 0.143
  26. MR Imaging Radiomics Signatures for Predicting the Risk of Breast Cancer Recurrence as Given by Research Versions of MammaPrint, Oncotype DX, and PAM50 Gene Assays. Radiology. 2016 Nov; 281(2):382-391.
    View in: PubMed
    Score: 0.142
  27. Automated Breast Ultrasound in Breast Cancer Screening of Women With Dense Breasts: Reader Study of Mammography-Negative and Mammography-Positive Cancers. AJR Am J Roentgenol. 2016 Jun; 206(6):1341-50.
    View in: PubMed
    Score: 0.142
  28. Using computer-extracted image phenotypes from tumors on breast magnetic resonance imaging to predict breast cancer pathologic stage. Cancer. 2016 Mar 01; 122(5):748-57.
    View in: PubMed
    Score: 0.138
  29. Using quantitative image analysis to classify axillary lymph nodes on breast MRI: a new application for the Z 0011 Era. Eur J Radiol. 2015 Mar; 84(3):392-397.
    View in: PubMed
    Score: 0.129
  30. Mammographic quantitative image analysis and biologic image composition for breast lesion characterization and classification. Med Phys. 2014 Mar; 41(3):031915.
    View in: PubMed
    Score: 0.122
  31. Quantitative ultrasound image analysis of axillary lymph node status in breast cancer patients. Int J Comput Assist Radiol Surg. 2013 Nov; 8(6):895-903.
    View in: PubMed
    Score: 0.115
  32. A novel hybrid linear/nonlinear classifier for two-class classification: theory, algorithm, and applications. IEEE Trans Med Imaging. 2010 Feb; 29(2):428-41.
    View in: PubMed
    Score: 0.090
  33. Robustness of computerized lesion detection and classification scheme across different breast US platforms. Radiology. 2005 Dec; 237(3):834-40.
    View in: PubMed
    Score: 0.069
  34. Computerized detection and classification of cancer on breast ultrasound. Acad Radiol. 2004 May; 11(5):526-35.
    View in: PubMed
    Score: 0.062
  35. Computerized analysis of shadowing on breast ultrasound for improved lesion detection. Med Phys. 2003 Jul; 30(7):1833-42.
    View in: PubMed
    Score: 0.058
  36. Computerized lesion detection on breast ultrasound. Med Phys. 2002 Jul; 29(7):1438-46.
    View in: PubMed
    Score: 0.055
  37. Dual-energy three-compartment breast imaging for compositional biomarkers to improve detection of malignant lesions. Commun Med (Lond). 2021; 1:29.
    View in: PubMed
    Score: 0.052
  38. Radiogenomics of breast cancer using dynamic contrast enhanced MRI and gene expression profiling. Cancer Imaging. 2019 Jul 15; 19(1):48.
    View in: PubMed
    Score: 0.044
  39. PROSTATEx Challenges for computerized classification of prostate lesions from multiparametric magnetic resonance images. J Med Imaging (Bellingham). 2018 Oct; 5(4):044501.
    View in: PubMed
    Score: 0.042
  40. Breast MRI radiomics: comparison of computer- and human-extracted imaging phenotypes. Eur Radiol Exp. 2017; 1(1):22.
    View in: PubMed
    Score: 0.040
  41. Letter to the Editor: Use of Publicly Available Image Resources. Acad Radiol. 2017 07; 24(7):916-917.
    View in: PubMed
    Score: 0.038
  42. LUNGx Challenge for computerized lung nodule classification. J Med Imaging (Bellingham). 2016 Oct; 3(4):044506.
    View in: PubMed
    Score: 0.037
  43. Deciphering Genomic Underpinnings of Quantitative MRI-based Radiomic Phenotypes of Invasive Breast Carcinoma. Sci Rep. 2015 Dec 07; 5:17787.
    View in: PubMed
    Score: 0.035
  44. Prediction of clinical phenotypes in invasive breast carcinomas from the integration of radiomics and genomics data. J Med Imaging (Bellingham). 2015 10; 2(4):041007.
    View in: PubMed
    Score: 0.034
  45. LUNGx Challenge for computerized lung nodule classification: reflections and lessons learned. J Med Imaging (Bellingham). 2015 Apr; 2(2):020103.
    View in: PubMed
    Score: 0.033
  46. Impact of lesion segmentation metrics on computer-aided diagnosis/detection in breast computed tomography. J Med Imaging (Bellingham). 2014 Oct; 1(3):031012.
    View in: PubMed
    Score: 0.032
  47. Segmentation of breast masses on dedicated breast computed tomography and three-dimensional breast ultrasound images. J Med Imaging (Bellingham). 2014 Apr; 1(1):014501.
    View in: PubMed
    Score: 0.031
  48. TU-E-217BCD-07: Pilot Study on Consistency in Size Metrics for a Multimodality PEM/MR Breast Imaging Approach. Med Phys. 2012 Jun; 39(6Part24):3915.
    View in: PubMed
    Score: 0.027
  49. Enhancement of breast CADx with unlabeled dataa). Med Phys. 2010 Aug; 37(8):4155-4172.
    View in: PubMed
    Score: 0.024
  50. Enhancement of breast CADx with unlabeled data. Med Phys. 2010 Aug; 37(8):4155-72.
    View in: PubMed
    Score: 0.024
  51. Exploring nonlinear feature space dimension reduction and data representation in breast Cadx with Laplacian eigenmaps and t-SNE. Med Phys. 2010 Jan; 37(1):339-51.
    View in: PubMed
    Score: 0.023
  52. Breast US computer-aided diagnosis system: robustness across urban populations in South Korea and the United States. Radiology. 2009 Dec; 253(3):661-71.
    View in: PubMed
    Score: 0.023
  53. Performance of breast ultrasound computer-aided diagnosis: dependence on image selection. Acad Radiol. 2008 Oct; 15(10):1234-45.
    View in: PubMed
    Score: 0.021
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.