TY - JOUR
T1 - Searching Images for Consensus
T2 - Can AI Remove Observer Variability in Pathology?
AU - Tizhoosh, Hamid R.
AU - Diamandis, Phedias
AU - Campbell, Clinton J.V.
AU - Safarpoor, Amir
AU - Kalra, Shivam
AU - Maleki, Danial
AU - Riasatian, Abtin
AU - Babaie, Morteza
N1 - Publisher Copyright:
© 2021 American Society for Investigative Pathology
PY - 2021/10
Y1 - 2021/10
N2 - One of the major obstacles in reaching diagnostic consensus is observer variability. With the recent success of artificial intelligence, particularly the deep networks, the question emerges as to whether the fundamental challenge of diagnostic imaging can now be resolved. This article briefly reviews the problem and how eventually both supervised and unsupervised AI technologies could help to overcome it.
AB - One of the major obstacles in reaching diagnostic consensus is observer variability. With the recent success of artificial intelligence, particularly the deep networks, the question emerges as to whether the fundamental challenge of diagnostic imaging can now be resolved. This article briefly reviews the problem and how eventually both supervised and unsupervised AI technologies could help to overcome it.
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U2 - 10.1016/j.ajpath.2021.01.015
DO - 10.1016/j.ajpath.2021.01.015
M3 - Review article
C2 - 33636179
AN - SCOPUS:85105917606
SN - 0002-9440
VL - 191
SP - 1702
EP - 1708
JO - American Journal of Pathology
JF - American Journal of Pathology
IS - 10
ER -