Deep Learning for Automated Triaging of 4581 Breast MRI Examinations from the DENSE Trial-Reference-Cited by-同舟云学术

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Deep Learning for Automated Triaging of 4581 Breast MRI Examinations from the DENSE Trial

Published:2022-01 Issue:1 Volume:302 Page:29-36
ISSN:0033-8419
Container-title:Radiology
language:en
Short-container-title:Radiology

Author:

Verburg Erik¹^ORCID,van Gils Carla H.¹^ORCID,van der Velden Bas H. M.¹^ORCID,Bakker Marije F.¹^ORCID,Pijnappel Ruud M.¹,Veldhuis Wouter B.¹^ORCID,Gilhuijs Kenneth G. A.¹

Affiliation:

1. From the Image Sciences Institute (E.V., B.H.M.v.d.V., K.G.A.G.), Julius Center for Health Sciences and Primary Care (C.H.v.G., M.F.B.), and Department of Radiology (R.M.P., W.B.V.), University Medical Center Utrecht, Utrecht University, Utrecht 3584 CX, the Netherlands.

Funder

KWF Kankerbestrijding

University Medical Center Utrecht

Publisher

Radiological Society of North America (RSNA)

Subject

Radiology, Nuclear Medicine and imaging

Link

http://pubs.rsna.org/doi/pdf/10.1148/radiol.2021203960

Reference33 articles.

1. Analysis of cancers missed at screening mammography.

2. Mammographic Density and the Risk and Detection of Breast Cancer

3. The Mammogram That Cried Wolfe

4. Volumetric breast density affects performance of digital screening mammography

5. The California Breast Density Information Group: A Collaborative Response to the Issues of Breast Density, Breast Cancer Risk, and Breast Density Notification Legislation

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