ResearchTrend.AI
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

Jiesi Hu
Yanwu Yang
Xutao Guo
Jinghua Wang
Heather Ting Ma
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