Co-Authors
This is a "connection" page, showing publications co-authored by Maryellen L. Giger and Karen Drukker.
Connection Strength
7.863
-
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.
Score: 0.776
-
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.
Score: 0.701
-
Computerized detection of breast cancer on automated breast ultrasound imaging of women with dense breasts. Med Phys. 2014 Jan; 41(1):012901.
Score: 0.521
-
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.
Score: 0.496
-
Repeatability in computer-aided diagnosis: application to breast cancer diagnosis on sonography. Med Phys. 2010 Jun; 37(6):2659-69.
Score: 0.407
-
Repeatability in computer-aided diagnosis: Application to breast cancer diagnosis on sonography. Med Phys. 2010 Jun; 37(6Part1):2659-2669.
Score: 0.407
-
Automated method for improving system performance of computer-aided diagnosis in breast ultrasound. IEEE Trans Med Imaging. 2009 Jan; 28(1):122-8.
Score: 0.369
-
Breast US computer-aided diagnosis workstation: performance with a large clinical diagnostic population. Radiology. 2008 Aug; 248(2):392-7.
Score: 0.355
-
Multimodality computerized diagnosis of breast lesions using mammography and sonography. Acad Radiol. 2005 Aug; 12(8):970-9.
Score: 0.291
-
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: 0.233
-
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.
Score: 0.223
-
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: 0.219
-
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: 0.186
-
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.
Score: 0.184
-
Deep learning in medical imaging and radiation therapy. Med Phys. 2019 Jan; 46(1):e1-e36.
Score: 0.183
-
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: 0.176
-
Fuzzy c-means segmentation of major vessels in angiographic images of stroke. J Med Imaging (Bellingham). 2018 Jan; 5(1):014501.
Score: 0.172
-
Quantitative MRI radiomics in the prediction of molecular classifications of breast cancer subtypes in the TCGA/TCIA data set. NPJ Breast Cancer. 2016; 2.
Score: 0.153
-
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.
Score: 0.153
-
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.
Score: 0.152
-
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.
Score: 0.149
-
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.
Score: 0.139
-
Mammographic quantitative image analysis and biologic image composition for breast lesion characterization and classification. Med Phys. 2014 Mar; 41(3):031915.
Score: 0.132
-
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.
Score: 0.124
-
A novel hybrid linear/nonlinear classifier for two-class classification: theory, algorithm, and applications. IEEE Trans Med Imaging. 2010 Feb; 29(2):428-41.
Score: 0.097
-
Robustness of computerized lesion detection and classification scheme across different breast US platforms. Radiology. 2005 Dec; 237(3):834-40.
Score: 0.074
-
Computerized detection and classification of cancer on breast ultrasound. Acad Radiol. 2004 May; 11(5):526-35.
Score: 0.067
-
Computerized analysis of shadowing on breast ultrasound for improved lesion detection. Med Phys. 2003 Jul; 30(7):1833-42.
Score: 0.063
-
Computerized lesion detection on breast ultrasound. Med Phys. 2002 Jul; 29(7):1438-46.
Score: 0.059
-
Dual-energy three-compartment breast imaging for compositional biomarkers to improve detection of malignant lesions. Commun Med (Lond). 2021; 1:29.
Score: 0.055
-
Radiogenomics of breast cancer using dynamic contrast enhanced MRI and gene expression profiling. Cancer Imaging. 2019 Jul 15; 19(1):48.
Score: 0.048
-
PROSTATEx Challenges for computerized classification of prostate lesions from multiparametric magnetic resonance images. J Med Imaging (Bellingham). 2018 Oct; 5(4):044501.
Score: 0.046
-
Breast MRI radiomics: comparison of computer- and human-extracted imaging phenotypes. Eur Radiol Exp. 2017; 1(1):22.
Score: 0.043
-
Letter to the Editor: Use of Publicly Available Image Resources. Acad Radiol. 2017 07; 24(7):916-917.
Score: 0.041
-
LUNGx Challenge for computerized lung nodule classification. J Med Imaging (Bellingham). 2016 Oct; 3(4):044506.
Score: 0.040
-
Deciphering Genomic Underpinnings of Quantitative MRI-based Radiomic Phenotypes of Invasive Breast Carcinoma. Sci Rep. 2015 Dec 07; 5:17787.
Score: 0.037
-
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.
Score: 0.037
-
LUNGx Challenge for computerized lung nodule classification: reflections and lessons learned. J Med Imaging (Bellingham). 2015 Apr; 2(2):020103.
Score: 0.036
-
Impact of lesion segmentation metrics on computer-aided diagnosis/detection in breast computed tomography. J Med Imaging (Bellingham). 2014 Oct; 1(3):031012.
Score: 0.035
-
Segmentation of breast masses on dedicated breast computed tomography and three-dimensional breast ultrasound images. J Med Imaging (Bellingham). 2014 Apr; 1(1):014501.
Score: 0.033
-
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.
Score: 0.029
-
Enhancement of breast CADx with unlabeled dataa). Med Phys. 2010 Aug; 37(8):4155-4172.
Score: 0.026
-
Enhancement of breast CADx with unlabeled data. Med Phys. 2010 Aug; 37(8):4155-72.
Score: 0.026
-
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.
Score: 0.025
-
Breast US computer-aided diagnosis system: robustness across urban populations in South Korea and the United States. Radiology. 2009 Dec; 253(3):661-71.
Score: 0.024
-
Performance of breast ultrasound computer-aided diagnosis: dependence on image selection. Acad Radiol. 2008 Oct; 15(10):1234-45.
Score: 0.023