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Towards Generalizable Medical Image Segmentation with Pixel-wise
  Uncertainty Estimation
v1v2v3 (latest)

Towards Generalizable Medical Image Segmentation with Pixel-wise Uncertainty Estimation

13 May 2023
Shuai Wang
Zipei Yan
Daoan Zhang
Zhong Li
Sirui Wu
Wen-Xiang Chen
Ruizhen Li
    UQCV
ArXiv (abs)PDFHTML

Papers citing "Towards Generalizable Medical Image Segmentation with Pixel-wise Uncertainty Estimation"

8 / 8 papers shown
Title
Generalizable Medical Image Segmentation via Random Amplitude Mixup and
  Domain-Specific Image Restoration
Generalizable Medical Image Segmentation via Random Amplitude Mixup and Domain-Specific Image Restoration
Ziqi Zhou
Lei Qi
Yinghuan Shi
105
34
0
08 Aug 2022
DoFE: Domain-oriented Feature Embedding for Generalizable Fundus Image
  Segmentation on Unseen Datasets
DoFE: Domain-oriented Feature Embedding for Generalizable Fundus Image Segmentation on Unseen Datasets
Shujun Wang
Lequan Yu
Kang Li
Xin Yang
Chi-Wing Fu
Pheng-Ann Heng
61
137
0
13 Oct 2020
FDA: Fourier Domain Adaptation for Semantic Segmentation
FDA: Fourier Domain Adaptation for Semantic Segmentation
Yanchao Yang
Stefano Soatto
OOD
89
898
0
11 Apr 2020
REFUGE Challenge: A Unified Framework for Evaluating Automated Methods
  for Glaucoma Assessment from Fundus Photographs
REFUGE Challenge: A Unified Framework for Evaluating Automated Methods for Glaucoma Assessment from Fundus Photographs
J. Orlando
Huazhu Fu
J. Barbosa-Breda
K. V. Keer
Deepti R. Bathula
...
Fei Li
Xiulan Zhang
Yanwu Xu
Xiulan Zhang
Hrvoje Bogunović
131
618
0
08 Oct 2019
CutMix: Regularization Strategy to Train Strong Classifiers with
  Localizable Features
CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Features
Sangdoo Yun
Dongyoon Han
Seong Joon Oh
Sanghyuk Chun
Junsuk Choe
Y. Yoo
OOD
620
4,798
0
13 May 2019
mixup: Beyond Empirical Risk Minimization
mixup: Beyond Empirical Risk Minimization
Hongyi Zhang
Moustapha Cissé
Yann N. Dauphin
David Lopez-Paz
NoLa
282
9,797
0
25 Oct 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
359
4,718
0
15 Mar 2017
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
831
9,345
0
06 Jun 2015
1