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A Chebyshev Confidence Guided Source-Free Domain Adaptation Framework
  for Medical Image Segmentation

A Chebyshev Confidence Guided Source-Free Domain Adaptation Framework for Medical Image Segmentation

27 October 2023
Jiesi Hu
Yanwu Yang
Xutao Guo
Jinghua Wang
Heather Ting Ma
    MedIm
ArXivPDFHTML

Papers citing "A Chebyshev Confidence Guided Source-Free Domain Adaptation Framework for Medical Image Segmentation"

3 / 3 papers shown
Title
In Defense of Pseudo-Labeling: An Uncertainty-Aware Pseudo-label
  Selection Framework for Semi-Supervised Learning
In Defense of Pseudo-Labeling: An Uncertainty-Aware Pseudo-label Selection Framework for Semi-Supervised Learning
Mamshad Nayeem Rizve
Kevin Duarte
Y. S. Rawat
M. Shah
203
501
0
15 Jan 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
247
9,042
0
06 Jun 2015
U-Net: Convolutional Networks for Biomedical Image Segmentation
U-Net: Convolutional Networks for Biomedical Image Segmentation
Olaf Ronneberger
Philipp Fischer
Thomas Brox
SSeg
3DV
229
74,467
0
18 May 2015
1