Co-Authors
This is a "connection" page, showing publications co-authored by Maryellen L. Giger and Hui Li.
Connection Strength
10.417
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Predicting intensive care need for COVID-19 patients using deep learning on chest radiography. J Med Imaging (Bellingham). 2023 Jul; 10(4):044504.
Score: 0.993
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Temporal Machine Learning Analysis of Prior Mammograms for Breast Cancer Risk Prediction. Cancers (Basel). 2023 Apr 04; 15(7).
Score: 0.967
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Impact of continuous learning on diagnostic breast MRI AI: evaluation on an independent clinical dataset. J Med Imaging (Bellingham). 2022 May; 9(3):034502.
Score: 0.913
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Digital Mammography in Breast Cancer: Additive Value of Radiomics of Breast Parenchyma. Radiology. 2019 04; 291(1):15-20.
Score: 0.726
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Breast density estimation from high spectral and spatial resolution MRI. J Med Imaging (Bellingham). 2016 Oct; 3(4):044507.
Score: 0.626
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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: 0.600
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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: 0.599
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Radiomic and deep learning characterization of breast parenchyma on full field digital mammograms and specimen radiographs: a pilot study of a potential cancer field effect. J Med Imaging (Bellingham). 2023 Jul; 10(4):044501.
Score: 0.246
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A machine-learning algorithm for distinguishing malignant from benign indeterminate thyroid nodules using ultrasound radiomic features. J Med Imaging (Bellingham). 2022 May; 9(3):034501.
Score: 0.228
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A review of explainable and interpretable AI with applications in COVID-19 imaging. Med Phys. 2022 Jan; 49(1):1-14.
Score: 0.221
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Lessons learned in transitioning to AI in the medical imaging of COVID-19. J Med Imaging (Bellingham). 2021 Jan; 8(Suppl 1):010902-10902.
Score: 0.218
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Multi-Stage Harmonization for Robust AI across Breast MR Databases. Cancers (Basel). 2021 Sep 26; 13(19).
Score: 0.218
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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.
Score: 0.209
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Harmonization of radiomic features of breast lesions across international DCE-MRI datasets. J Med Imaging (Bellingham). 2020 Jan; 7(1):012707.
Score: 0.195
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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.
Score: 0.191
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Independent validation of machine learning in diagnosing breast Cancer on magnetic resonance imaging within a single institution. Cancer Imaging. 2019 Sep 18; 19(1):64.
Score: 0.189
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Radiomics robustness assessment and classification evaluation: A two-stage method demonstrated on multivendor FFDM. Med Phys. 2019 May; 46(5):2145-2156.
Score: 0.182
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Breast lesion classification based on dynamic contrast-enhanced magnetic resonance images sequences with long short-term memory networks. J Med Imaging (Bellingham). 2019 Jan; 6(1):011002.
Score: 0.176
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Transfer Learning From Convolutional Neural Networks for Computer-Aided Diagnosis: A Comparison of Digital Breast Tomosynthesis and Full-Field Digital Mammography. Acad Radiol. 2019 06; 26(6):735-743.
Score: 0.175
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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: 0.171
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Quantitative texture analysis: robustness of radiomics across two digital mammography manufacturers' systems. J Med Imaging (Bellingham). 2018 Jan; 5(1):011002.
Score: 0.165
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Deep learning in breast cancer risk assessment: evaluation of convolutional neural networks on a clinical dataset of full-field digital mammograms. J Med Imaging (Bellingham). 2017 Oct; 4(4):041304.
Score: 0.164
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Digital mammographic tumor classification using transfer learning from deep convolutional neural networks. J Med Imaging (Bellingham). 2016 Jul; 3(3):034501.
Score: 0.153
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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: 0.145
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Comparative analysis of image-based phenotypes of mammographic density and parenchymal patterns in distinguishing between BRCA1/2 cases, unilateral cancer cases, and controls. J Med Imaging (Bellingham). 2014 Oct; 1(3):031009.
Score: 0.135
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Relationships between computer-extracted mammographic texture pattern features and BRCA1/2 mutation status: a cross-sectional study. Breast Cancer Res. 2014; 16(4):424.
Score: 0.133
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Pilot study demonstrating potential association between breast cancer image-based risk phenotypes and genomic biomarkers. Med Phys. 2014 Mar; 41(3):031917.
Score: 0.129
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Computerized analysis of mammographic parenchymal patterns on a large clinical dataset of full-field digital mammograms: robustness study with two high-risk datasets. J Digit Imaging. 2012 Oct; 25(5):591-8.
Score: 0.117
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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: 0.114
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Evaluation of computer-aided diagnosis on a large clinical full-field digital mammographic dataset. Acad Radiol. 2008 Nov; 15(11):1437-45.
Score: 0.089
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Power spectral analysis of mammographic parenchymal patterns for breast cancer risk assessment. J Digit Imaging. 2008 Jun; 21(2):145-52.
Score: 0.084
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Fractal analysis of mammographic parenchymal patterns in breast cancer risk assessment. Acad Radiol. 2007 May; 14(5):513-21.
Score: 0.080
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Computerized texture analysis of mammographic parenchymal patterns of digitized mammograms. Acad Radiol. 2005 Jul; 12(7):863-73.
Score: 0.071
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Computerized analysis of mammographic parenchymal patterns for assessing breast cancer risk: effect of ROI size and location. Med Phys. 2004 Mar; 31(3):549-55.
Score: 0.064
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MIDRC CRP10 AI interface-an integrated tool for exploring, testing and visualization of AI models. Phys Med Biol. 2023 03 23; 68(7).
Score: 0.060
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Differences in Molecular Subtype Reference Standards Impact AI-based Breast Cancer Classification with Dynamic Contrast-enhanced MRI. Radiology. 2023 04; 307(1):e220984.
Score: 0.059
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Radiogenomics of breast cancer using dynamic contrast enhanced MRI and gene expression profiling. Cancer Imaging. 2019 Jul 15; 19(1):48.
Score: 0.047
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Prognostic value of pre-treatment CT texture analysis in combination with change in size of the primary tumor in response to induction chemotherapy for HPV-positive oropharyngeal squamous cell carcinoma. Quant Imaging Med Surg. 2019 Mar; 9(3):399-408.
Score: 0.046
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Variation in algorithm implementation across radiomics software. J Med Imaging (Bellingham). 2018 Oct; 5(4):044505.
Score: 0.045
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Breast MRI radiomics: comparison of computer- and human-extracted imaging phenotypes. Eur Radiol Exp. 2017; 1(1):22.
Score: 0.042
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Fast bilateral breast coverage with high spectral and spatial resolution (HiSS) MRI at 3T. J Magn Reson Imaging. 2017 11; 46(5):1341-1348.
Score: 0.040
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Deciphering Genomic Underpinnings of Quantitative MRI-based Radiomic Phenotypes of Invasive Breast Carcinoma. Sci Rep. 2015 Dec 07; 5:17787.
Score: 0.036
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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: 0.036
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Mammographic quantitative image analysis and biologic image composition for breast lesion characterization and classification. Med Phys. 2014 Mar; 41(3):031915.
Score: 0.032
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Potential of computer-aided diagnosis of high spectral and spatial resolution (HiSS) MRI in the classification of breast lesions. J Magn Reson Imaging. 2014 Jan; 39(1):59-67.
Score: 0.031
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Computerized three-class classification of MRI-based prognostic markers for breast cancer. Phys Med Biol. 2011 Sep 21; 56(18):5995-6008.
Score: 0.027
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Combined use of T2-weighted MRI and T1-weighted dynamic contrast-enhanced MRI in the automated analysis of breast lesions. Magn Reson Med. 2011 Aug; 66(2):555-64.
Score: 0.026
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Normal parenchymal enhancement patterns in women undergoing MR screening of the breast. Eur Radiol. 2011 Jul; 21(7):1374-82.
Score: 0.026
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Multimodality computer-aided breast cancer diagnosis with FFDM and DCE-MRI. Acad Radiol. 2010 Sep; 17(9):1158-67.
Score: 0.025
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Computerized assessment of breast lesion malignancy using DCE-MRI robustness study on two independent clinical datasets from two manufacturers. Acad Radiol. 2010 Jul; 17(7):822-9.
Score: 0.025
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Cancerous breast lesions on dynamic contrast-enhanced MR images: computerized characterization for image-based prognostic markers. Radiology. 2010 Mar; 254(3):680-90.
Score: 0.024
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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: 0.024
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Correlative feature analysis on FFDM. Med Phys. 2008 Dec; 35(12):5490-500.
Score: 0.022
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A dual-stage method for lesion segmentation on digital mammograms. Med Phys. 2007 Nov; 34(11):4180-93.
Score: 0.021
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Volumetric texture analysis of breast lesions on contrast-enhanced magnetic resonance images. Magn Reson Med. 2007 Sep; 58(3):562-71.
Score: 0.021
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Comparison of radiographic texture analysis from computed radiography and bone densitometry systems. Med Phys. 2004 Apr; 31(4):882-91.
Score: 0.016