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Automated quantification of myocardial tissue characteristics from
  native T1 mapping using neural networks with Bayesian inference for
  uncertainty-based quality-control

Automated quantification of myocardial tissue characteristics from native T1 mapping using neural networks with Bayesian inference for uncertainty-based quality-control

31 January 2020
Esther Puyol-Antón
B. Ruijsink
Christian F. Baumgartner
Matthew Sinclair
E. Konukoglu
Reza Razavi
A. King
ArXivPDFHTML

Papers citing "Automated quantification of myocardial tissue characteristics from native T1 mapping using neural networks with Bayesian inference for uncertainty-based quality-control"

4 / 4 papers shown
Title
FUSQA: Fetal Ultrasound Segmentation Quality Assessment
FUSQA: Fetal Ultrasound Segmentation Quality Assessment
Sevim Cengiz
Ibrahim Almakk
Mohammad Yaqub
32
0
0
08 Mar 2023
Trustworthy clinical AI solutions: a unified review of uncertainty
  quantification in deep learning models for medical image analysis
Trustworthy clinical AI solutions: a unified review of uncertainty quantification in deep learning models for medical image analysis
Benjamin Lambert
Florence Forbes
A. Tucholka
Senan Doyle
Harmonie Dehaene
M. Dojat
38
82
0
05 Oct 2022
Efficient Model Monitoring for Quality Control in Cardiac Image
  Segmentation
Efficient Model Monitoring for Quality Control in Cardiac Image Segmentation
Francesco Galati
Maria A. Zuluaga
27
15
0
12 Apr 2021
Simple and Scalable Predictive Uncertainty Estimation using Deep
  Ensembles
Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles
Balaji Lakshminarayanan
Alexander Pritzel
Charles Blundell
UQCV
BDL
287
5,707
0
05 Dec 2016
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