Co-Authors
This is a "connection" page, showing publications co-authored by Hiroyuki Abe and Maryellen L. Giger.
Connection Strength
1.106
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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.
Score: 0.239
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Use of clinical MRI maximum intensity projections for improved breast lesion classification with deep convolutional neural networks. J Med Imaging (Bellingham). 2018 Jan; 5(1):014503.
Score: 0.163
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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: 0.158
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Breast density estimation from high spectral and spatial resolution MRI. J Med Imaging (Bellingham). 2016 Oct; 3(4):044507.
Score: 0.151
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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: 0.131
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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: 0.116
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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: 0.045
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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: 0.038
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Evaluation of clinical breast MR imaging performed with prototype computer-aided diagnosis breast MR imaging workstation: reader study. Radiology. 2011 Mar; 258(3):696-704.
Score: 0.025
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DCEMRI of breast lesions: is kinetic analysis equally effective for both mass and nonmass-like enhancement? Med Phys. 2008 Jul; 35(7):3102-9.
Score: 0.021
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DCEMRI of breast lesions: Is kinetic analysis equally effective for both mass and nonmass-like enhancement? Med Phys. 2008 Jul; 35(7Part1):3102-3109.
Score: 0.021