Multi-view cross-consistency and multi-scale cross-layer contrastive learning for semi-supervised medical image segmentation
Authors:
- Xunhang CaoXCXunhang Cao
- Xiaoxin GuoXGXiaoxin Guo
- Guangqi YangGYGuangqi Yang
- Qi ChenQCQi Chen
- Hongliang DongHDHongliang Dong
- 17 March 2025
- journal article
- Published by Elsevier in Expert Systems with Applications
- Vol. 277
Abstract
No abstract availableFunding Information
- Key Research and Development Program of Jiangxi Province (20220201141GX)
- National Natural Science Foundation of China (82071995)
This publication has 28 references indexed in Scilit:
- Semi-supervised medical image segmentation via a tripled-uncertainty guided mean teacher model with contrastive learningMedical Image Analysis, 2022
- Deep co-training for semi-supervised image segmentationPattern Recognition, 2020
- Shape-Aware Semi-supervised 3D Semantic Segmentation for Medical Images2020
- Momentum Contrast for Unsupervised Visual Representation Learning2020
- Computer‐aided diagnosis in the era of deep learningMedical Physics, 2020
- Uncertainty-Aware Self-ensembling Model for Semi-supervised 3D Left Atrium Segmentation2019
- Recurrent Saliency Transformation Network: Incorporating Multi-stage Visual Cues for Small Organ Segmentation2018
- Data Distillation: Towards Omni-Supervised Learning2018
- Evaluation of prostate segmentation algorithms for MRI: The PROMISE12 challengeMedical Image Analysis, 2014
- Self-labeled techniques for semi-supervised learning: taxonomy, software and empirical studyKnowledge and Information Systems, 2013