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Co-Authors

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

7.863
  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: 0.776
  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: 0.701
  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: 0.521
  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: 0.496
  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: 0.407
  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: 0.407
  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: 0.369
  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: 0.355
  9. Multimodality computerized diagnosis of breast lesions using mammography and sonography. Acad Radiol. 2005 Aug; 12(8):970-9.
    View in: PubMed
    Score: 0.291
  10. 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: 0.233
  11. 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: 0.223
  12. 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: 0.219
  13. 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: 0.186
  14. 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: 0.184
  15. Deep learning in medical imaging and radiation therapy. Med Phys. 2019 Jan; 46(1):e1-e36.
    View in: PubMed
    Score: 0.183
  16. 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: 0.176
  17. 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: 0.172
  18. 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: 0.153
  19. 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: 0.153
  20. 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: 0.152
  21. 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: 0.149
  22. 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: 0.139
  23. 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: 0.132
  24. 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: 0.124
  25. 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: 0.097
  26. Robustness of computerized lesion detection and classification scheme across different breast US platforms. Radiology. 2005 Dec; 237(3):834-40.
    View in: PubMed
    Score: 0.074
  27. Computerized detection and classification of cancer on breast ultrasound. Acad Radiol. 2004 May; 11(5):526-35.
    View in: PubMed
    Score: 0.067
  28. Computerized analysis of shadowing on breast ultrasound for improved lesion detection. Med Phys. 2003 Jul; 30(7):1833-42.
    View in: PubMed
    Score: 0.063
  29. Computerized lesion detection on breast ultrasound. Med Phys. 2002 Jul; 29(7):1438-46.
    View in: PubMed
    Score: 0.059
  30. 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: 0.055
  31. 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: 0.048
  32. 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: 0.046
  33. Breast MRI radiomics: comparison of computer- and human-extracted imaging phenotypes. Eur Radiol Exp. 2017; 1(1):22.
    View in: PubMed
    Score: 0.043
  34. Letter to the Editor: Use of Publicly Available Image Resources. Acad Radiol. 2017 07; 24(7):916-917.
    View in: PubMed
    Score: 0.041
  35. LUNGx Challenge for computerized lung nodule classification. J Med Imaging (Bellingham). 2016 Oct; 3(4):044506.
    View in: PubMed
    Score: 0.040
  36. Deciphering Genomic Underpinnings of Quantitative MRI-based Radiomic Phenotypes of Invasive Breast Carcinoma. Sci Rep. 2015 Dec 07; 5:17787.
    View in: PubMed
    Score: 0.037
  37. 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: 0.037
  38. LUNGx Challenge for computerized lung nodule classification: reflections and lessons learned. J Med Imaging (Bellingham). 2015 Apr; 2(2):020103.
    View in: PubMed
    Score: 0.036
  39. 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: 0.035
  40. 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: 0.033
  41. 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: 0.029
  42. Enhancement of breast CADx with unlabeled dataa). Med Phys. 2010 Aug; 37(8):4155-4172.
    View in: PubMed
    Score: 0.026
  43. Enhancement of breast CADx with unlabeled data. Med Phys. 2010 Aug; 37(8):4155-72.
    View in: PubMed
    Score: 0.026
  44. 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: 0.025
  45. 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: 0.024
  46. Performance of breast ultrasound computer-aided diagnosis: dependence on image selection. Acad Radiol. 2008 Oct; 15(10):1234-45.
    View in: PubMed
    Score: 0.023
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.