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Mammography Reading with Computer-Aided Detection (CAD): Performance of Different Readers

  • Susan M. Astley
  • Stephen W. Duffy
  • Caroline R. M. Boggis
  • Mary Wilson
  • Nicky B. Barr
  • Ursula M. Beetles
  • Miriam A. Griffiths
  • Anil Jain
  • Jill Johnson
  • Rita M. Roberts
  • Heather Deans
  • Karen Duncan
  • Geeta Iyengar
  • Olorunsola Agbaje
  • Pamela M. Griffiths
  • Magnus A. McGee
  • Maureen G. C. Gillan
  • Fiona J. Gilbert
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4046)

Abstract

Computer-aided detection (CAD) systems place prompts in digital images to attract readers’ attention to potential malignancies. A reader must then decide whether or not prompted regions correspond to genuine abnormalities and has the option of disregarding falsely prompted regions. In this paper we investigate different readers’ performance with CAD in the context of breast screening. In a retrospective study, eight consultant radiologists each read over 1000 screening mammograms comprising normal cases, screen detected cancer cases and cases that were detected as cancers subsequently. We present their results in terms of cancer detection and recall rates, and relate this to their previous experience of film reading. Our results show that the detection of cancers did not differ significantly between readers, although more experienced film readers were less likely to recommend that normal cases should be recalled.

Keywords

Normal Case Recall Rate Breast Screening Cancer Detection Rate Single Reading 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Susan M. Astley
    • 1
  • Stephen W. Duffy
    • 2
  • Caroline R. M. Boggis
    • 3
  • Mary Wilson
    • 3
  • Nicky B. Barr
    • 3
  • Ursula M. Beetles
    • 3
  • Miriam A. Griffiths
    • 3
  • Anil Jain
    • 3
  • Jill Johnson
    • 3
  • Rita M. Roberts
    • 3
  • Heather Deans
    • 4
  • Karen Duncan
    • 4
  • Geeta Iyengar
    • 4
  • Olorunsola Agbaje
    • 2
  • Pamela M. Griffiths
    • 1
  • Magnus A. McGee
    • 5
  • Maureen G. C. Gillan
    • 6
  • Fiona J. Gilbert
    • 6
  1. 1.Department of Imaging Science & Biomedical EngineeringUniversity of ManchesterManchesterUK
  2. 2.Department of EpidemiologyMathematics & Statistics, Wolfson Institute of Preventive MedicineLondonUK
  3. 3.Nightingale CentreWithington HospitalManchesterUK
  4. 4.NE Scotland Breast Screening CentreAberdeenUK
  5. 5.Department of Public Health & General PracticeChristchurch School of MedicineNZ
  6. 6.Department of RadiologyUniversity of AberdeenUK

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