These tools will no longer be maintained as of December 31, 2024. Archived website can be found here. PubMed4Hh GitHub repository can be found here. Contact NLM Customer Service if you have questions.


BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

127 related articles for article (PubMed ID: 26409521)

  • 1. Parameter optimization of a computer-aided diagnosis system for detection of masses on digitized mammograms.
    Radovic M; Milosevic M; Ninkovic S; Filipovic N; Peulic A
    Technol Health Care; 2015; 23(6):757-74. PubMed ID: 26409521
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Incorporation of an iterative, linear segmentation routine into a mammographic mass CAD system.
    Catarious DM; Baydush AH; Floyd CE
    Med Phys; 2004 Jun; 31(6):1512-20. PubMed ID: 15259655
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Detection of mass regions in mammograms by bilateral analysis adapted to breast density using similarity indexes and convolutional neural networks.
    Bandeira Diniz JO; Bandeira Diniz PH; Azevedo Valente TL; Corrêa Silva A; de Paiva AC; Gattass M
    Comput Methods Programs Biomed; 2018 Mar; 156():191-207. PubMed ID: 29428071
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Dual system approach to computer-aided detection of breast masses on mammograms.
    Wei J; Chan HP; Sahiner B; Hadjiiski LM; Helvie MA; Roubidoux MA; Zhou C; Ge J
    Med Phys; 2006 Nov; 33(11):4157-68. PubMed ID: 17153394
    [TBL] [Abstract][Full Text] [Related]  

  • 5. Computer-aided diagnosis of masses with full-field digital mammography.
    Li L; Clark RA; Thomas JA
    Acad Radiol; 2002 Jan; 9(1):4-12. PubMed ID: 11918357
    [TBL] [Abstract][Full Text] [Related]  

  • 6. 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
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Location of mammograms ROI's and reduction of false-positive.
    Salazar-Licea LA; Pedraza-Ortega JC; Pastrana-Palma A; Aceves-Fernandez MA
    Comput Methods Programs Biomed; 2017 May; 143():97-111. PubMed ID: 28391823
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Mutual information-based template matching scheme for detection of breast masses: from mammography to digital breast tomosynthesis.
    Mazurowski MA; Lo JY; Harrawood BP; Tourassi GD
    J Biomed Inform; 2011 Oct; 44(5):815-23. PubMed ID: 21554985
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Reduction of False-Positive Markings on Mammograms: a Retrospective Comparison Study Using an Artificial Intelligence-Based CAD.
    Mayo RC; Kent D; Sen LC; Kapoor M; Leung JWT; Watanabe AT
    J Digit Imaging; 2019 Aug; 32(4):618-624. PubMed ID: 30963339
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Development of tolerant features for characterization of masses in mammograms.
    Rojas-Domínguez A; Nandi AK
    Comput Biol Med; 2009 Aug; 39(8):678-88. PubMed ID: 19524221
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Performance and reproducibility of a computerized mass detection scheme for digitized mammography using rotated and resampled images: an assessment.
    Zheng B; Maitz GS; Ganott MA; Abrams G; Leader JK; Gur D
    AJR Am J Roentgenol; 2005 Jul; 185(1):194-8. PubMed ID: 15972422
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Computer-aided breast cancer detection and diagnosis of masses using difference of Gaussians and derivative-based feature saliency.
    Polakowski WE; Cournoyer DA; Rogers SK; DeSimio MP; Ruck DW; Hoffmeister JW; Raines RA
    IEEE Trans Med Imaging; 1997 Dec; 16(6):811-9. PubMed ID: 9533581
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Simultaneous detection and classification of breast masses in digital mammograms via a deep learning YOLO-based CAD system.
    Al-Masni MA; Al-Antari MA; Park JM; Gi G; Kim TY; Rivera P; Valarezo E; Choi MT; Han SM; Kim TS
    Comput Methods Programs Biomed; 2018 Apr; 157():85-94. PubMed ID: 29477437
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Bilateral analysis based false positive reduction for computer-aided mass detection.
    Wu YT; Wei J; Hadjiiski LM; Sahiner B; Zhou C; Ge J; Shi J; Zhang Y; Chan HP
    Med Phys; 2007 Aug; 34(8):3334-44. PubMed ID: 17879797
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Computerized detection of masses in digitized mammograms using single-image segmentation and a multilayer topographic feature analysis.
    Zheng B; Chang YH; Gur D
    Acad Radiol; 1995 Nov; 2(11):959-66. PubMed ID: 9419667
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Improving mass candidate detection in mammograms via feature maxima propagation and local feature selection.
    Melendez J; Sánchez CI; van Ginneken B; Karssemeijer N
    Med Phys; 2014 Aug; 41(8):081904. PubMed ID: 25086535
    [TBL] [Abstract][Full Text] [Related]  

  • 17. A fully integrated computer-aided diagnosis system for digital X-ray mammograms via deep learning detection, segmentation, and classification.
    Al-Antari MA; Al-Masni MA; Choi MT; Han SM; Kim TS
    Int J Med Inform; 2018 Sep; 117():44-54. PubMed ID: 30032964
    [TBL] [Abstract][Full Text] [Related]  

  • 18. A novel computer aided breast mass detection scheme based on morphological enhancement and SLIC superpixel segmentation.
    Chu J; Min H; Liu L; Lu W
    Med Phys; 2015 Jul; 42(7):3859-69. PubMed ID: 26133587
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Computer-assisted detection of mammographic masses: a template matching scheme based on mutual information.
    Tourassi GD; Vargas-Voracek R; Catarious DM; Floyd CE
    Med Phys; 2003 Aug; 30(8):2123-30. PubMed ID: 12945977
    [TBL] [Abstract][Full Text] [Related]  

  • 20. An approach to the detection of lesions in mammograms using fuzzy image processing.
    Bayram B; Acar U
    J Int Med Res; 2007; 35(6):790-5. PubMed ID: 18034992
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 7.