Evaluation of Combined Artificial Intelligence and Radiologist Assessment to Interpret Screening Mammograms
Abstrak
This diagnostic accuracy study evaluates whether artificial intelligence can overcome human mammography interpretation limits with a rigorous, unbiased evaluation of machine learning algorithms.
Topik & Kata Kunci
Penulis (73)
T. Schaffter
D. Buist
Christoph I. Lee
Yaroslav Nikulin
D. Ribli
Y. Guan
William Lotter
Zequn Jie
Hao Du
Sijia Wang
Jiashi Feng
Mengling Feng
Hyo-Eun Kim
F. Albiol
A. Albiol
Stephen Morrell
Z. Wojna
M. Ahsen
U. Asif
Antonio Jose Jimeno Yepes
Shivanthan A. C. Yohanandan
S. Rabinovici-Cohen
Darvin Yi
B. Hoff
Thomas Yu
E. Chaibub Neto
D. Rubin
Peter Lindholm
L. Margolies
R. McBride
J. Rothstein
W. Sieh
Rami Ben-Ari
S. Harrer
A. Trister
S. Friend
Thea C. Norman
B. Sahiner
Fredrik Strand
J. Guinney
G. Stolovitzky
Lester W. Mackey
Joyce Cahoon
Li Shen
J. Sohn
H. Trivedi
Yiqiu Shen
L. Buturovic
José Costa Pereira
Jaime S. Cardoso
Eduardo Castro
K. T. Kalleberg
Obioma Pelka
Imane Nedjar
Krzysztof J. Geras
F. Nensa
Ethan Goan
S. Koitka
Luis Caballero
David D. Cox
Pavitra Krishnaswamy
G. Pandey
C. Friedrich
Dimitri Perrin
C. Fookes
Bibo Shi
Gerard Cardoso Negrie
Michael Kawczynski
Kyunghyun Cho
Can Son Khoo
Joseph Y. Lo
A. Sorensen
Hwejin Jung
Akses Cepat
- Tahun Terbit
- 2020
- Bahasa
- en
- Total Sitasi
- 336×
- Sumber Database
- Semantic Scholar
- DOI
- 10.1001/jamanetworkopen.2020.0265
- Akses
- Open Access ✓