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Majority Voting of Doctors Improves Appropriateness of AI Reliance in
  Pathology

Majority Voting of Doctors Improves Appropriateness of AI Reliance in Pathology

6 April 2024
H. Gu
Chunxu Yang
S. Magaki
Neda Zarrin-Khameh
N. Lakis
Inma Cobos
Negar Khanlou
Xinhai R. Zhang
Jasmeet Assi
Joshua T. Byers
Ameer Hamza
Karam Han
Anders Meyer
Hilda Mirbaha
Carrie A Mohila
Todd M. Stevens
Sara L. Stone
Wenzhong Yan
Mohammad Haeri
Xiang Ánthony' Chen
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Papers citing "Majority Voting of Doctors Improves Appropriateness of AI Reliance in Pathology"

3 / 3 papers shown
Title
NuCLS: A scalable crowdsourcing, deep learning approach and dataset for
  nucleus classification, localization and segmentation
NuCLS: A scalable crowdsourcing, deep learning approach and dataset for nucleus classification, localization and segmentation
M. Amgad
Lamees A. Atteya
Hagar Hussein
K. Mohammed
Ehab Hafiz
...
Critical Care
David Manthey
Atlanta
D. Neurology
Lurie Cancer Center
32
73
0
18 Feb 2021
Designing AI for Trust and Collaboration in Time-Constrained Medical
  Decisions: A Sociotechnical Lens
Designing AI for Trust and Collaboration in Time-Constrained Medical Decisions: A Sociotechnical Lens
Maia L. Jacobs
Jeffrey He
Melanie F. Pradier
Barbara D. Lam
Andrew C Ahn
T. McCoy
R. Perlis
Finale Doshi-Velez
Krzysztof Z. Gajos
49
144
0
01 Feb 2021
Dropout as a Bayesian Approximation: Representing Model Uncertainty in
  Deep Learning
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning
Y. Gal
Zoubin Ghahramani
UQCV
BDL
285
9,138
0
06 Jun 2015
1