BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

781 related articles for article (PubMed ID: 25186394)

  • 1. Using computer-extracted image features for modeling of error-making patterns in detection of mammographic masses among radiology residents.
    Zhang J; Lo JY; Kuzmiak CM; Ghate SV; Yoon SC; Mazurowski MA
    Med Phys; 2014 Sep; 41(9):091907. PubMed ID: 25186394
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Modeling false positive error making patterns in radiology trainees for improved mammography education.
    Zhang J; Silber JI; Mazurowski MA
    J Biomed Inform; 2015 Apr; 54():50-7. PubMed ID: 25640462
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Predicting error in detecting mammographic masses among radiology trainees using statistical models based on BI-RADS features.
    Grimm LJ; Ghate SV; Yoon SC; Kuzmiak CM; Kim C; Mazurowski MA
    Med Phys; 2014 Mar; 41(3):031909. PubMed ID: 24593727
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Identifying error-making patterns in assessment of mammographic BI-RADS descriptors among radiology residents using statistical pattern recognition.
    Mazurowski MA; Barnhart HX; Baker JA; Tourassi GD
    Acad Radiol; 2012 Jul; 19(7):865-71. PubMed ID: 22459643
    [TBL] [Abstract][Full Text] [Related]  

  • 5. Investigation of optimal use of computer-aided detection systems: the role of the "machine" in decision making process.
    Paquerault S; Hardy PT; Wersto N; Chen J; Smith RC
    Acad Radiol; 2010 Sep; 17(9):1112-21. PubMed ID: 20605489
    [TBL] [Abstract][Full Text] [Related]  

  • 6. External validation of a publicly available computer assisted diagnostic tool for mammographic mass lesions with two high prevalence research datasets.
    Benndorf M; Burnside ES; Herda C; Langer M; Kotter E
    Med Phys; 2015 Aug; 42(8):4987-96. PubMed ID: 26233224
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Predicting diagnostic error in radiology via eye-tracking and image analytics: preliminary investigation in mammography.
    Voisin S; Pinto F; Morin-Ducote G; Hudson KB; Tourassi GD
    Med Phys; 2013 Oct; 40(10):101906. PubMed ID: 24089908
    [TBL] [Abstract][Full Text] [Related]  

  • 8. A model-based framework for the detection of spiculated masses on mammography.
    Sampat MP; Bovik AC; Whitman GJ; Markey MK
    Med Phys; 2008 May; 35(5):2110-23. PubMed ID: 18561687
    [TBL] [Abstract][Full Text] [Related]  

  • 9. A completely automated CAD system for mass detection in a large mammographic database.
    Bellotti R; De Carlo F; Tangaro S; Gargano G; Maggipinto G; Castellano M; Massafra R; Cascio D; Fauci F; Magro R; Raso G; Lauria A; Forni G; Bagnasco S; Cerello P; Zanon E; Cheran SC; Lopez Torres E; Bottigli U; Masala GL; Oliva P; Retico A; Fantacci ME; Cataldo R; De Mitri I; De Nunzio G
    Med Phys; 2006 Aug; 33(8):3066-75. PubMed ID: 16964885
    [TBL] [Abstract][Full Text] [Related]  

  • 10. 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
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Radiology Trainee Performance in Digital Breast Tomosynthesis: Relationship Between Difficulty and Error-Making Patterns.
    Grimm LJ; Zhang J; Lo JY; Johnson KS; Ghate SV; Walsh R; Mazurowski MA
    J Am Coll Radiol; 2016 Feb; 13(2):198-202. PubMed ID: 26577878
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Influences of Radiology Trainees on Screening Mammography Interpretation.
    Hawley JR; Taylor CR; Cubbison AM; Erdal BS; Yildiz VO; Carkaci S
    J Am Coll Radiol; 2016 May; 13(5):554-61. PubMed ID: 26924162
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Individualized computer-aided education in mammography based on user modeling: concept and preliminary experiments.
    Mazurowski MA; Baker JA; Barnhart HX; Tourassi GD
    Med Phys; 2010 Mar; 37(3):1152-60. PubMed ID: 20384251
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Improvement of mammographic mass characterization using spiculation meausures and morphological features.
    Sahiner B; Chan HP; Petrick N; Helvie MA; Hadjiiski LM
    Med Phys; 2001 Jul; 28(7):1455-65. PubMed ID: 11488579
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Parenchymal texture analysis in digital mammography: A fully automated pipeline for breast cancer risk assessment.
    Zheng Y; Keller BM; Ray S; Wang Y; Conant EF; Gee JC; Kontos D
    Med Phys; 2015 Jul; 42(7):4149-60. PubMed ID: 26133615
    [TBL] [Abstract][Full Text] [Related]  

  • 16. 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]  

  • 17. 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; 37(10):1479-91. PubMed ID: 17383623
    [TBL] [Abstract][Full Text] [Related]  

  • 18. 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
    [TBL] [Abstract][Full Text] [Related]  

  • 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; 25(5):570-9. PubMed ID: 22234836
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Comparison of Chest Radiograph Interpretations by Artificial Intelligence Algorithm vs Radiology Residents.
    Wu JT; Wong KCL; Gur Y; Ansari N; Karargyris A; Sharma A; Morris M; Saboury B; Ahmad H; Boyko O; Syed A; Jadhav A; Wang H; Pillai A; Kashyap S; Moradi M; Syeda-Mahmood T
    JAMA Netw Open; 2020 Oct; 3(10):e2022779. PubMed ID: 33034642
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 40.