Exploring Smoothness and Class-Separation for Semi-supervised Medical Image Segmentation
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- 16 September 2022
- book chapter
- Published by Springer Nature
- p. 34-43
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This publication has 21 references indexed in Scilit:
- Shape-Aware Semi-supervised 3D Semantic Segmentation for Medical Images2020
- Semi-Supervised Semantic Segmentation With Cross-Consistency Training2020
- Transformation-Consistent Self-Ensembling Model for Semisupervised Medical Image SegmentationIEEE Transactions on Neural Networks and Learning Systems, 2020
- 3D Semi-Supervised Learning with Uncertainty-Aware Multi-View Co-Training2020
- Keypoint based weakly supervised human parsingImage and Vision Computing, 2019
- Uncertainty-Aware Self-ensembling Model for Semi-supervised 3D Left Atrium Segmentation2019
- Virtual Adversarial Training: A Regularization Method for Supervised and Semi-Supervised LearningIEEE Transactions on Pattern Analysis and Machine Intelligence, 2018
- Deep Learning Techniques for Automatic MRI Cardiac Multi-Structures Segmentation and Diagnosis: Is the Problem Solved?IEEE Transactions on Medical Imaging, 2018
- Invited Talk: U-Net Convolutional Networks for Biomedical Image Segmentation2017
- V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation2016