Deep Learning in Breast Cancer Screening-Reference-Cited by-同舟云学术

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Deep Learning in Breast Cancer Screening

Published:2019 Issue: Volume: Page:187-215
ISSN:
Container-title:Artificial Intelligence in Medical Imaging
language:
Short-container-title:

Author:

Harvey Hugh,Heindl Andreas,Khara Galvin,Korkinof Dimitrios,O’Neill Michael,Yearsley Joseph,Karpati Edith,Rijken Tobias,Kecskemethy Peter,Forrai Gabor

Publisher

Springer International Publishing

Link

http://link.springer.com/content/pdf/10.1007/978-3-319-94878-2_14

Reference120 articles.

1. Ferlay J, Steliarova-Foucher E, Lortet-Tieulent J, Rosso S, Coebergh JWW, Comber H, Forman D, Bray F. Cancer incidence and mortality patterns in Europe: estimates for 40 countries in 2012. Eur J Cancer. 2013;49(6):1374–1403.

2. Tabár L, Gad A, Holmberg LH, Ljungquist U, Fagerberg CJG, Baldetorp L, Gröntoft O, Lundström B, Månson JC, Eklund G, Day NE, Pettersson F. Reduction in mortality from breast cancer after mass screening with mammography: randomised trial from the breast cancer screening working group of the Swedish National Board of Health and Welfare. Lancet. 1985;325(8433):829–32.

3. Lee CH, David Dershaw D, Kopans D, Evans P, Monsees B, Monticciolo D, James Brenner R, Bassett L, Berg W, Feig S, Hendrick E, Mendelson E, D’Orsi C, Sickles E, Burhenne LW. Breast cancer screening with imaging: recommendations from the society of breast imaging and the ACR on the use of mammography, breast MRI, breast ultrasound, and other technologies for the detection of clinically occult breast cancer. J Am Coll Radiol. 2010;7(1):18–27.

4. Boyer B, Balleyguier C, Granat O, Pharaboz C. CAD in questions/answers: review of the literature. Eur J Radiol. 2009;69(1):24–33.

5. Duijm LEM, Louwman MWJ, Groenewoud JH, Van De Poll-Franse LV, Fracheboud J, Coebergh JW. Inter-observer variability in mammography screening and effect of type and number of readers on screening outcome. Br J Cancer. 2009;100(6): 901–7.

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