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Automated Muscle Segmentation from Clinical CT using Bayesian U-Net for
  Personalized Musculoskeletal Modeling
v1v2 (latest)

Automated Muscle Segmentation from Clinical CT using Bayesian U-Net for Personalized Musculoskeletal Modeling

21 July 2019
Yuta Hiasa
Y. Otake
Masaki Takao
Takeshi Ogawa
Nobuhiko Sugano
Yoshinobu Sato
ArXiv (abs)PDFHTML

Papers citing "Automated Muscle Segmentation from Clinical CT using Bayesian U-Net for Personalized Musculoskeletal Modeling"

12 / 12 papers shown
Title
Exploring Uncertainty Measures in Deep Networks for Multiple Sclerosis
  Lesion Detection and Segmentation
Exploring Uncertainty Measures in Deep Networks for Multiple Sclerosis Lesion Detection and Segmentation
T. Nair
Doina Precup
Douglas L. Arnold
Tal Arbel
UQCV
58
445
0
03 Aug 2018
Towards safe deep learning: accurately quantifying biomarker uncertainty
  in neural network predictions
Towards safe deep learning: accurately quantifying biomarker uncertainty in neural network predictions
Zach Eaton-Rosen
Felix J. S. Bragman
Sotirios Bisdas
Sebastien Ourselin
M. Jorge Cardoso
UQCV
63
86
0
22 Jun 2018
Cross-modality image synthesis from unpaired data using CycleGAN:
  Effects of gradient consistency loss and training data size
Cross-modality image synthesis from unpaired data using CycleGAN: Effects of gradient consistency loss and training data size
Yuta Hiasa
Y. Otake
Masaki Takao
Takumi Matsuoka
K. Takashima
Jerry L. Prince
Nobuhiko Sugano
Yoshinobu Sato
GANMedIm
45
199
0
18 Mar 2018
Suggestive Annotation: A Deep Active Learning Framework for Biomedical
  Image Segmentation
Suggestive Annotation: A Deep Active Learning Framework for Biomedical Image Segmentation
Ling Yang
Yizhe Zhang
Jianxu Chen
Siyuan Zhang
Danny Chen
MedIm
71
504
0
15 Jun 2017
What Uncertainties Do We Need in Bayesian Deep Learning for Computer
  Vision?
What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?
Alex Kendall
Y. Gal
BDLOODUDUQCVPER
354
4,709
0
15 Mar 2017
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
64
127
0
11 Feb 2017
Fully Convolutional Networks for Semantic Segmentation
Fully Convolutional Networks for Semantic Segmentation
Evan Shelhamer
Jonathan Long
Trevor Darrell
VOSSSeg
741
37,862
0
20 May 2016
Bayesian SegNet: Model Uncertainty in Deep Convolutional Encoder-Decoder
  Architectures for Scene Understanding
Bayesian SegNet: Model Uncertainty in Deep Convolutional Encoder-Decoder Architectures for Scene Understanding
Alex Kendall
Vijay Badrinarayanan
R. Cipolla
UQCVBDL
89
1,065
0
09 Nov 2015
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
UQCVBDL
821
9,318
0
06 Jun 2015
Delving Deep into Rectifiers: Surpassing Human-Level Performance on
  ImageNet Classification
Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification
Kaiming He
Xinming Zhang
Shaoqing Ren
Jian Sun
VLM
326
18,625
0
06 Feb 2015
Adam: A Method for Stochastic Optimization
Adam: A Method for Stochastic Optimization
Diederik P. Kingma
Jimmy Ba
ODL
1.8K
150,115
0
22 Dec 2014
Multi-Atlas Segmentation of Biomedical Images: A Survey
Multi-Atlas Segmentation of Biomedical Images: A Survey
Juan Eugenio Iglesias
M. Sabuncu
87
671
0
10 Dec 2014
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