A Chebyshev Confidence Guided Source-Free Domain Adaptation Framework for Medical Image Segmentation
Author:
Affiliation:
1. School of Electronics and Information Engineering, Harbin Institute of Technology at Shenzhen, Shenzhen, China
2. School of Computer Science and Technology, Harbin Institute of Technology at Shenzhen, Shenzhen, China
Funder
National Natural Science Foundation of China
The Major Key Project of PCL
overseas High-Caliber Personnel Start-up funding
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Link
http://xplorestaging.ieee.org/ielx7/6221020/10666866/10541072.pdf?arnumber=10541072
Reference54 articles.
1. U-Net: Convolutional Networks for Biomedical Image Segmentation
2. UNet++: Redesigning Skip Connections to Exploit Multiscale Features in Image Segmentation
3. Deep Residual Learning for Image Recognition
4. DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs
5. Memory consistent unsupervised off-the-shelf model adaptation for source-relaxed medical image segmentation
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