Co-Authors
This is a "connection" page, showing publications co-authored by Bin Zheng and Hong Liu.
Connection Strength
5.454
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A Pilot Study to Assess the Performance of Phase-Sensitive Breast Tomosynthesis. Radiology. 2023 Feb; 306(2):e213198.
Score: 0.212
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Evaluation and comparison of a CdTe based photon counting detector with an energy integrating detector for X-ray phase sensitive imaging of breast cancer. J Xray Sci Technol. 2022; 30(2):207-219.
Score: 0.202
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A phase sensitive x-ray breast tomosynthesis system: Preliminary patient images with cancer lesions. Phys Med Biol. 2021 10 29; 66(21).
Score: 0.199
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Development and preclinical evaluation of a patient-specific high energy x-ray phase sensitive breast tomosynthesis system. Med Phys. 2021 May; 48(5):2511-2520.
Score: 0.192
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Impact of a single distance phase retrieval algorithm on spatial resolution in X-ray inline phase sensitive imaging. Biomed Spectrosc Imaging. 2019 Jul 09; 8(1-2):29-40.
Score: 0.166
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Applying a new computer-aided detection scheme generated imaging marker to predict short-term breast cancer risk. Phys Med Biol. 2018 05 15; 63(10):105005.
Score: 0.157
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Detectability comparison of simulated tumors in digital breast tomosynthesis using high-energy X-ray inline phase sensitive and commercial imaging systems. Phys Med. 2018 Mar; 47:34-41.
Score: 0.155
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Prediction of breast cancer risk using a machine learning approach embedded with a locality preserving projection algorithm. Phys Med Biol. 2018 01 30; 63(3):035020.
Score: 0.154
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Tutorial on X-ray photon counting detector characterization. J Xray Sci Technol. 2018; 26(1):1-28.
Score: 0.153
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Quantitative investigation of the edge enhancement in in-line phase contrast projections and tomosynthesis provided by distributing microbubbles on the interface between two tissues: a phantom study. Phys Med Biol. 2017 Nov 21; 62(24):9357-9376.
Score: 0.152
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Detectability comparison between a high energy x-ray phase sensitive and mammography systems in imaging phantoms with varying glandular-adipose ratios. Phys Med Biol. 2017 05 07; 62(9):3523-3538.
Score: 0.145
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A two-step convolutional neural network based computer-aided detection scheme for automatically segmenting adipose tissue volume depicting on CT images. Comput Methods Programs Biomed. 2017 Jun; 144:97-104.
Score: 0.145
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Characterization of Continuous and Pulsed Emission modes of a Hybrid Micro Focus X-ray Source for Medical Imaging Applications. Nucl Instrum Methods Phys Res A. 2017 May 01; 853:70-77.
Score: 0.144
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A new approach to develop computer-aided diagnosis scheme of breast mass classification using deep learning technology. J Xray Sci Technol. 2017; 25(5):751-763.
Score: 0.143
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Noise Power Characteristics of a Micro-Computed Tomography System. J Comput Assist Tomogr. 2017 Jan; 41(1):82-89.
Score: 0.143
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Applying a computer-aided scheme to detect a new radiographic image marker for prediction of chemotherapy outcome. BMC Med Imaging. 2016 08 31; 16(1):52.
Score: 0.139
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Practical alignment method for X-ray spectral measurement in micro-CT system based on 3D printing technology. Biomed Phys Eng Express. 2016 Jun; 2(3).
Score: 0.138
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Quantitative measurement of adiposity using CT images to predict the benefit of bevacizumab-based chemotherapy in epithelial ovarian cancer patients. Oncol Lett. 2016 Jul; 12(1):680-686.
Score: 0.137
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Investigation of spatial resolution characteristics of an in vivo micro computed tomography system. Nucl Instrum Methods Phys Res A. 2016 Jan 21; 807:129-136.
Score: 0.134
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The impact of spectral filtration on image quality in micro-CT system. J Appl Clin Med Phys. 2016 01 08; 17(1):301-315.
Score: 0.133
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Early prediction of clinical benefit of treating ovarian cancer using quantitative CT image feature analysis. Acta Radiol. 2016 Sep; 57(9):1149-55.
Score: 0.133
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Computer-aided breast MR image feature analysis for prediction of tumor response to chemotherapy. Med Phys. 2015 Nov; 42(11):6520-8.
Score: 0.132
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Assessment of performance and reproducibility of applying a content-based image retrieval scheme for classification of breast lesions. Med Phys. 2015 Jul; 42(7):4241-9.
Score: 0.129
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A new approach to develop computer-aided detection schemes of digital mammograms. Phys Med Biol. 2015 Jun 07; 60(11):4413-27.
Score: 0.128
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Characterization of a high-energy in-line phase contrast tomosynthesis prototype. Med Phys. 2015 May; 42(5):2404-20.
Score: 0.127
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Assessment of a Four-View Mammographic Image Feature Based Fusion Model to Predict Near-Term Breast Cancer Risk. Ann Biomed Eng. 2015 Oct; 43(10):2416-28.
Score: 0.127
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Feature selection for the automated detection of metaphase chromosomes: performance comparison using a receiver operating characteristic method. Anal Cell Pathol (Amst). 2014; 2014:565392.
Score: 0.123
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Detection of posteriorly located breast tumors using gold nanoparticles: a breast-mimicking phantom study. J Xray Sci Technol. 2014; 22(6):785-96.
Score: 0.116
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Evaluations of auto-focusing methods under a microscopic imaging modality for metaphase chromosome image analysis. Anal Cell Pathol (Amst). 2013; 36(1-2):37-44.
Score: 0.108
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The impact of the condenser on cytogenetic image quality in digital microscope system. Anal Cell Pathol (Amst). 2013; 36(1-2):45-59.
Score: 0.108
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Simultaneous dual-color fluorescence microscope: a characterization study. Anal Cell Pathol (Amst). 2013; 36(5-6):163-72.
Score: 0.108
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Impact of the optical depth of field on cytogenetic image quality. J Biomed Opt. 2012 Sep; 17(9):96017-1.
Score: 0.106
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Automated detection and analysis of fluorescent in situ hybridization spots depicted in digital microscopic images of Pap-smear specimens. J Biomed Opt. 2009 Mar-Apr; 14(2):021002.
Score: 0.083
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Development and Assessment of an Integrated Computer-Aided Detection Scheme for Digital Microscopic Images of Metaphase Chromosomes. J Electron Imaging. 2008 Oct 01; 17(4).
Score: 0.081
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Automated classification of metaphase chromosomes: optimization of an adaptive computerized scheme. J Biomed Inform. 2009 Feb; 42(1):22-31.
Score: 0.079
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A rule-based computer scheme for centromere identification and polarity assignment of metaphase chromosomes. Comput Methods Programs Biomed. 2008 Jan; 89(1):33-42.
Score: 0.076
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Automated identification of analyzable metaphase chromosomes depicted on microscopic digital images. J Biomed Inform. 2008 Apr; 41(2):264-71.
Score: 0.074
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A computer-aided method to expedite the evaluation of prognosis for childhood acute lymphoblastic leukemia. Technol Cancer Res Treat. 2006 Aug; 5(4):429-36.
Score: 0.069
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Transformers Improve Breast Cancer Diagnosis from Unregistered Multi-View Mammograms. Diagnostics (Basel). 2022 Jun 25; 12(7).
Score: 0.052
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Recent advances and clinical applications of deep learning in medical image analysis. Med Image Anal. 2022 07; 79:102444.
Score: 0.051
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Using Fourier ptychography microscopy to achieve high-resolution chromosome imaging: an initial evaluation. J Biomed Opt. 2022 01; 27(1).
Score: 0.050
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Correlation of imaging and plasma based biomarkers to predict response to bevacizumab in epithelial ovarian cancer (EOC). Gynecol Oncol. 2021 05; 161(2):382-388.
Score: 0.048
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Developing a new radiomics-based CT image marker to detect lymph node metastasis among cervical cancer patients. Comput Methods Programs Biomed. 2020 Dec; 197:105759.
Score: 0.046
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Applying a new quantitative image analysis scheme based on global mammographic features to assist diagnosis of breast cancer. Comput Methods Programs Biomed. 2019 Oct; 179:104995.
Score: 0.043
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Prediction of chemotherapy response in ovarian cancer patients using a new clustered quantitative image marker. Phys Med Biol. 2018 08 06; 63(15):155020.
Score: 0.040
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Classification of Tumor Epithelium and Stroma by Exploiting Image Features Learned by Deep Convolutional Neural Networks. Ann Biomed Eng. 2018 Dec; 46(12):1988-1999.
Score: 0.040
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Applying Quantitative CT Image Feature Analysis to Predict Response of Ovarian Cancer Patients to Chemotherapy. Acad Radiol. 2017 10; 24(10):1233-1239.
Score: 0.037