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Journal Abstract Search

393 related items for PubMed ID: 27911353

  • 1. Computer-aided classification of mammographic masses using visually sensitive image features.
    Wang Y, Aghaei F, Zarafshani A, Qiu Y, Qian W, Zheng B.
    J Xray Sci Technol; 2017; 25(1):171-186. PubMed ID: 27911353
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  • 3. Applying a new quantitative image analysis scheme based on global mammographic features to assist diagnosis of breast cancer.
    Chen X, Zargari A, Hollingsworth AB, Liu H, Zheng B, Qiu Y.
    Comput Methods Programs Biomed; 2019 Oct; 179():104995. PubMed ID: 31443864
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  • 5. False-positive reduction in computer-aided mass detection using mammographic texture analysis and classification.
    Dhahbi S, Barhoumi W, Kurek J, Swiderski B, Kruk M, Zagrouba E.
    Comput Methods Programs Biomed; 2018 Jul; 160():75-83. PubMed ID: 29728249
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  • 6. A new approach to develop computer-aided diagnosis scheme of breast mass classification using deep learning technology.
    Qiu Y, Yan S, Gundreddy RR, Wang Y, Cheng S, Liu H, Zheng B.
    J Xray Sci Technol; 2017 Jul; 25(5):751-763. PubMed ID: 28436410
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  • 7. A method to test the reproducibility and to improve performance of computer-aided detection schemes for digitized mammograms.
    Zheng B, Gur D, Good WF, Hardesty LA.
    Med Phys; 2004 Nov; 31(11):2964-72. PubMed ID: 15587648
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  • 11. Improving performance of computer-aided detection of masses by incorporating bilateral mammographic density asymmetry: an assessment.
    Wang X, Li L, Xu W, Liu W, Lederman D, Zheng B.
    Acad Radiol; 2012 Mar; 19(3):303-10. PubMed ID: 22173323
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  • 12. Computer-aided characterization of mammographic masses: accuracy of mass segmentation and its effects on characterization.
    Sahiner B, Petrick N, Chan HP, Hadjiiski LM, Paramagul C, Helvie MA, Gurcan MN.
    IEEE Trans Med Imaging; 2001 Dec; 20(12):1275-84. PubMed ID: 11811827
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  • 13. Detection of breast masses in mammograms by density slicing and texture flow-field analysis.
    Mudigonda NR, Rangayyan RM, Desautels JE.
    IEEE Trans Med Imaging; 2001 Dec; 20(12):1215-27. PubMed ID: 11811822
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  • 14. A new approach to develop computer-aided detection schemes of digital mammograms.
    Tan M, Qian W, Pu J, Liu H, Zheng B.
    Phys Med Biol; 2015 Jun 07; 60(11):4413-27. PubMed ID: 25984710
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  • 15. A method to improve visual similarity of breast masses for an interactive computer-aided diagnosis environment.
    Zheng B, Lu A, Hardesty LA, Sumkin JH, Hakim CM, Ganott MA, Gur D.
    Med Phys; 2006 Jan 07; 33(1):111-7. PubMed ID: 16485416
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  • 16. Characterization of mammographic masses using a gradient-based segmentation algorithm and a neural classifier.
    Delogu P, Evelina Fantacci M, Kasae P, Retico A.
    Comput Biol Med; 2007 Oct 07; 37(10):1479-91. PubMed ID: 17383623
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  • 17. Mammographic features of breast cancers at single reading with computer-aided detection and at double reading in a large multicenter prospective trial of computer-aided detection: CADET II.
    James JJ, Gilbert FJ, Wallis MG, Gillan MG, Astley SM, Boggis CR, Agbaje OF, Brentnall AR, Duffy SW.
    Radiology; 2010 Aug 07; 256(2):379-86. PubMed ID: 20656831
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  • 18. Improving performance of computer-aided detection scheme by combining results from two machine learning classifiers.
    Park SC, Pu J, Zheng B.
    Acad Radiol; 2009 Mar 07; 16(3):266-74. PubMed ID: 19201355
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  • 19. An interactive system for computer-aided diagnosis of breast masses.
    Wang X, Li L, Liu W, Xu W, Lederman D, Zheng B.
    J Digit Imaging; 2012 Oct 07; 25(5):570-9. PubMed ID: 22234836
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  • 20. Computer-aided detection of breast masses on mammograms: dual system approach with two-view analysis.
    Wei J, Chan HP, Sahiner B, Zhou C, Hadjiiski LM, Roubidoux MA, Helvie MA.
    Med Phys; 2009 Oct 07; 36(10):4451-60. PubMed ID: 19928076
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