Literature DB >> 15081844

Computer-aided detection in mammography.

S M Astley1, F J Gilbert.   

Abstract

Mammographic film reading for breast screening is a highly demanding visual task involving a detailed visual search for signs of abnormality, which are infrequent and often small or subtle. False-negative cases, in which a cancer is missed by a film reader, are known to occur. Although double reading has proved effective in reducing errors, there is a national shortage of film readers in the screening programme, and recent extensions to the programme have exacerbated this problem. The use of computer-aided detection (CAD) systems could potentially provide a solution by improving individual performance to the extent that double reading is no longer necessary. In this paper, we describe how CAD works, review the relevant literature and examine the strengths and weaknesses of the approach.

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Year:  2004        PMID: 15081844     DOI: 10.1016/j.crad.2003.11.017

Source DB:  PubMed          Journal:  Clin Radiol        ISSN: 0009-9260            Impact factor:   2.350


  13 in total

1.  Automated detection of mass lesions in dedicated breast CT: a preliminary study.

Authors:  I Reiser; R M Nishikawa; M L Giger; J M Boone; K K Lindfors; K Yang
Journal:  Med Phys       Date:  2012-02       Impact factor: 4.071

2.  Strategies to configure image analysis algorithms for clinical usage.

Authors:  Thomas M Lehmann; Jörg Bredno
Journal:  J Am Med Inform Assoc       Date:  2005-05-19       Impact factor: 4.497

Review 3.  CAD for mammography: the technique, results, current role and further developments.

Authors:  Ansgar Malich; Dorothee R Fischer; Joachim Böttcher
Journal:  Eur Radiol       Date:  2006-01-17       Impact factor: 5.315

4.  Characterization and classification of tumor lesions using computerized fractal-based texture analysis and support vector machines in digital mammograms.

Authors:  Qi Guo; Jiaqing Shao; Virginie F Ruiz
Journal:  Int J Comput Assist Radiol Surg       Date:  2008-10-28       Impact factor: 2.924

5.  Integrating CAD modules in a PACS environment using a wide computing infrastructure.

Authors:  Jorge J Suárez-Cuenca; Amara Tilve; Ricardo López; Gonzalo Ferro; Javier Quiles; Miguel Souto
Journal:  Int J Comput Assist Radiol Surg       Date:  2017-02-10       Impact factor: 2.924

6.  Current and Future Methods for Measuring Breast Density: A Brief Comparative Review.

Authors:  Mark A Sak; Peter J Littrup; Neb Duric; Maeve Mullooly; Mark E Sherman; Gretchen L Gierach
Journal:  Breast Cancer Manag       Date:  2015-08-28

7.  Is there a safety-net effect with computer-aided detection?

Authors:  Ethan Du-Crow; Susan M Astley; Johan Hulleman
Journal:  J Med Imaging (Bellingham)       Date:  2019-12-26

8.  Applying a new computer-aided detection scheme generated imaging marker to predict short-term breast cancer risk.

Authors:  Seyedehnafiseh Mirniaharikandehei; Alan B Hollingsworth; Bhavika Patel; Morteza Heidari; Hong Liu; Bin Zheng
Journal:  Phys Med Biol       Date:  2018-05-15       Impact factor: 3.609

9.  Counterpoint to "Performance assessment of diagnostic systems under the FROC paradigm" by Gur and Rockette.

Authors:  Dev P Chakraborty
Journal:  Acad Radiol       Date:  2009-04       Impact factor: 3.173

10.  CADe system integrated within the electronic health record.

Authors:  Noelia Vállez; Gloria Bueno; Óscar Déniz; María del Milagro Fernández; Carlos Pastor; Miguel Ángel Rienda; Pablo Esteve; María Arias
Journal:  Biomed Res Int       Date:  2013-09-17       Impact factor: 3.411

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