A Semiautonomous Deep Learning System to Reduce False Positives in Screening Mammography-Reference-Cited by-同舟云学术

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A Semiautonomous Deep Learning System to Reduce False Positives in Screening Mammography

Published:2024-05-01 Issue:3 Volume:6 Page:
ISSN:2638-6100
Container-title:Radiology: Artificial Intelligence
language:en
Short-container-title:Radiology: Artificial Intelligence

Author:

Pedemonte Stefano¹,Tsue Trevor¹^ORCID,Mombourquette Brent¹^ORCID,Truong Vu Yen Nhi¹,Matthews Thomas¹^ORCID,Morales Hoil Rodrigo¹^ORCID,Shah Meet¹,Ghare Nikita¹^ORCID,Zingman-Daniels Naomi¹^ORCID,Holley Susan¹,Appleton Catherine M.¹,Su Jason¹^ORCID,Wahl Richard L.¹^ORCID

Affiliation:

1. From Whiterabbit.ai, 3930 Freedom Cir, Santa Clara, CA 95054 (S.P., T.T., B.M., Y.N.T.V., T.M., R.M.H., M.S., N.G., N.Z.D., J.S.); Onsite Women's Health, Westfield, Mass (S.H.); SSM Health, St Louis, Mo (C.M.A.); and Mallinckrodt Institute of Radiology, Washington University School of Medicine, St Louis, Mo (R.L.W.).

Publisher

Radiological Society of North America (RSNA)

Link

http://pubs.rsna.org/doi/pdf/10.1148/ryai.230033

Reference32 articles.

1. Estimating the global cancer incidence and mortality in 2018: GLOBOCAN sources and methods

2. Periodic Screening for Breast Cancer: The HIP Randomized Controlled Trial

3. Mammographic screening and mortality from breast cancer: the Malmo mammographic screening trial.

4. Followup after 11 years – update of mortality results in the Stockholm mammographic screening trial

5. THE SWEDISH TWO-COUNTY TRIAL TWENTY YEARS LATER

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