Literature DB >> 10943274

Computer-aided detection and diagnosis of breast cancer.

C J Vyborny1, M L Giger, R M Nishikawa.   

Abstract

The limitations of radiologists when interpreting mammogram examinations provides a reasonable, if not compelling, basis for application of computer techniques that have the potential to improve diagnostic performance. Computer algorithms, at their present state of development, show great promise for clinical use. It can be expected that such use will only improve as computer technology and computer methods continue to become more formidable. The eventual role of computers in mammographic detection and diagnosis has not been fully defined, but their effect on practice may one day be very significant.

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Year:  2000        PMID: 10943274     DOI: 10.1016/s0033-8389(05)70197-4

Source DB:  PubMed          Journal:  Radiol Clin North Am        ISSN: 0033-8389            Impact factor:   2.303


  16 in total

1.  Can electronic zoom replace magnification in mammography? A comparative Monte Carlo study.

Authors:  M Koutalonis; H Delis; A Pascoal; G Spyrou; L Costaridou; G Panayiotakis
Journal:  Br J Radiol       Date:  2010-07       Impact factor: 3.039

2.  Comparison of algorithms to enhance spicules of spiculated masses on mammography.

Authors:  Mehul P Sampat; Gary J Whitman; Alan C Bovik; Mia K Markey
Journal:  J Digit Imaging       Date:  2008-03       Impact factor: 4.056

Review 3.  Anniversary paper: History and status of CAD and quantitative image analysis: the role of Medical Physics and AAPM.

Authors:  Maryellen L Giger; Heang-Ping Chan; John Boone
Journal:  Med Phys       Date:  2008-12       Impact factor: 4.071

4.  Computer aided automatic detection of malignant lesions in diffuse optical mammography.

Authors:  David R Busch; Wensheng Guo; Regine Choe; Turgut Durduran; Michael D Feldman; Carolyn Mies; Mark A Rosen; Mitchell D Schnall; Brian J Czerniecki; Julia Tchou; Angela DeMichele; Mary E Putt; Arjun G Yodh
Journal:  Med Phys       Date:  2010-04       Impact factor: 4.071

5.  Computer-aided diagnostic models in breast cancer screening.

Authors:  Turgay Ayer; Mehmet Us Ayvaci; Ze Xiu Liu; Oguzhan Alagoz; Elizabeth S Burnside
Journal:  Imaging Med       Date:  2010-06-01

6.  Breast cancer risk estimation with artificial neural networks revisited: discrimination and calibration.

Authors:  Turgay Ayer; Oguzhan Alagoz; Jagpreet Chhatwal; Jude W Shavlik; Charles E Kahn; Elizabeth S Burnside
Journal:  Cancer       Date:  2010-07-15       Impact factor: 6.860

7.  Evaluation of texture for classification of abdominal aortic aneurysm after endovascular repair.

Authors:  Guillermo García; Josu Maiora; Arantxa Tapia; Mariano De Blas
Journal:  J Digit Imaging       Date:  2012-06       Impact factor: 4.056

8.  Semiautomated three-dimensional segmentation software to quantify carpal bone volume changes on wrist CT scans for arthritis assessment.

Authors:  J Duryea; M Magalnick; S Alli; L Yao; M Wilson; R Goldbach-Mansky
Journal:  Med Phys       Date:  2008-06       Impact factor: 4.071

9.  Role of computer-aided detection in very small screening detected invasive breast cancers.

Authors:  Xavier Bargalló; Martín Velasco; Gorane Santamaría; Montse Del Amo; Pedro Arguis; Sonia Sánchez Gómez
Journal:  J Digit Imaging       Date:  2013-06       Impact factor: 4.056

10.  A logistic regression model based on the national mammography database format to aid breast cancer diagnosis.

Authors:  Jagpreet Chhatwal; Oguzhan Alagoz; Mary J Lindstrom; Charles E Kahn; Katherine A Shaffer; Elizabeth S Burnside
Journal:  AJR Am J Roentgenol       Date:  2009-04       Impact factor: 3.959

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