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Automatic quality control framework for more reliable integration of
  machine learning-based image segmentation into medical workflows

Automatic quality control framework for more reliable integration of machine learning-based image segmentation into medical workflows

6 December 2021
Elena Williams
Sebastian Niehaus
J. Reinelt
A. Merola
P. Mihai
K. Villringer
Konstantin Thierbach
Evelyn Medawar
Daniel Lichterfeld
Ingo Roeder
N. Scherf
Maria del C. Valdés Hernández
ArXivPDFHTML

Papers citing "Automatic quality control framework for more reliable integration of machine learning-based image segmentation into medical workflows"

2 / 2 papers shown
Title
Bayesian QuickNAT: Model Uncertainty in Deep Whole-Brain Segmentation
  for Structure-wise Quality Control
Bayesian QuickNAT: Model Uncertainty in Deep Whole-Brain Segmentation for Structure-wise Quality Control
Abhijit Guha Roy
Sailesh Conjeti
Nassir Navab
Christian Wachinger
UQCV
33
119
0
24 Nov 2018
Reverse Classification Accuracy: Predicting Segmentation Performance in
  the Absence of Ground Truth
Reverse Classification Accuracy: Predicting Segmentation Performance in the Absence of Ground Truth
V. Valindria
I. Lavdas
Wenjia Bai
Konstantinos Kamnitsas
E. Aboagye
A. Rockall
Daniel Rueckert
Ben Glocker
42
127
0
11 Feb 2017
1