Learning with limited annotations: a survey on deep semi-supervised learning for medical image segmentation

R Jiao, Y Zhang, L Ding, B Xue, J Zhang, R Cai… - Computers in Biology …, 2024 - Elsevier
Medical image segmentation is a fundamental and critical step in many image-guided
clinical approaches. Recent success of deep learning-based segmentation methods usually …

[HTML][HTML] Recent advancements in emerging technologies for healthcare management systems: a survey

SB Junaid, AA Imam, AO Balogun, LC De Silva… - Healthcare, 2022 - mdpi.com
In recent times, the growth of the Internet of Things (IoT), artificial intelligence (AI), and
Blockchain technologies have quickly gained pace as a new study niche in numerous …

Bidirectional copy-paste for semi-supervised medical image segmentation

Y Bai, D Chen, Q Li, W Shen… - Proceedings of the IEEE …, 2023 - openaccess.thecvf.com
In semi-supervised medical image segmentation, there exist empirical mismatch problems
between labeled and unlabeled data distribution. The knowledge learned from the labeled …

Ambiguity-selective consistency regularization for mean-teacher semi-supervised medical image segmentation

Z Xu, Y Wang, D Lu, X Luo, J Yan, Y Zheng… - Medical Image …, 2023 - Elsevier
Semi-supervised learning has greatly advanced medical image segmentation since it
effectively alleviates the need of acquiring abundant annotations from experts, wherein the …

Semi-supervised medical image segmentation via cross teaching between cnn and transformer

X Luo, M Hu, T Song, G Wang… - … conference on medical …, 2022 - proceedings.mlr.press
Recently, deep learning with Convolutional Neural Networks (CNNs) and Transformers has
shown encouraging results in fully supervised medical image segmentation. However, it is …