Deep semi-supervised learning for medical image segmentation: A review

K Han, VS Sheng, Y Song, Y Liu, C Qiu, S Ma… - Expert Systems with …, 2024 - Elsevier
Deep learning has recently demonstrated considerable promise for a variety of computer
vision tasks. However, in many practical applications, large-scale labeled datasets are not …

[HTML][HTML] Local contrastive loss with pseudo-label based self-training for semi-supervised medical image segmentation

K Chaitanya, E Erdil, N Karani, E Konukoglu - Medical image analysis, 2023 - Elsevier
Supervised deep learning-based methods yield accurate results for medical image
segmentation. However, they require large labeled datasets for this, and obtaining them is a …

Semi-supervised medical image segmentation using adversarial consistency learning and dynamic convolution network

T Lei, D Zhang, X Du, X Wang, Y Wan… - IEEE transactions on …, 2022 - ieeexplore.ieee.org
Popular semi-supervised medical image segmentation networks often suffer from error
supervision from unlabeled data since they usually use consistency learning under different …

Mine your own anatomy: Revisiting medical image segmentation with extremely limited labels

C You, W Dai, F Liu, Y Min, NC Dvornek… - … on Pattern Analysis …, 2024 - ieeexplore.ieee.org
Recent studies on contrastive learning have achieved remarkable performance solely by
leveraging few labels in medical image segmentation. Existing methods mainly focus on …

A contrastive consistency semi-supervised left atrium segmentation model

Y Liu, W Wang, G Luo, K Wang, S Li - Computerized Medical Imaging and …, 2022 - Elsevier
Accurate segmentation for the left atrium (LA) is a key process of clinical diagnosis and
therapy for atrial fibrillation. In clinical, the semantic-level segmentation of LA consumes …

Domain adaptive semantic segmentation via regional contrastive consistency regularization

Q Zhou, C Zhuang, R Yi, X Lu… - 2022 IEEE International …, 2022 - ieeexplore.ieee.org
Unsupervised domain adaptation (UDA) for semantic seg-mentation has been well-studied
in recent years. However, most existing works largely neglect the local regional consis-tency …

Semi-supervised spatial temporal attention network for video polyp segmentation

X Zhao, Z Wu, S Tan, DJ Fan, Z Li, X Wan… - … Conference on Medical …, 2022 - Springer
Deep learning-based polyp segmentation approaches have achieved great success in
image datasets. However, the frame-by-frame annotation of polyp videos requires a large …

Weakly-supervised 3D medical image segmentation using geometric prior and contrastive similarity

H Du, Q Dong, Y Xu, J Liao - IEEE Transactions on Medical …, 2023 - ieeexplore.ieee.org
Medical image segmentation is almost the most important pre-processing procedure in
computer-aided diagnosis but is also a very challenging task due to the complex shapes of …

USL-Net: Uncertainty self-learning network for unsupervised skin lesion segmentation

X Li, B Peng, J Hu, C Ma, D Yang, Z Xie - Biomedical Signal Processing …, 2024 - Elsevier
Unsupervised skin lesion segmentation offers several benefits, such as conserving expert
human resources, reducing discrepancies caused by subjective human labeling, and …

Ocaml scientific computing

L Wang, J Zhao, R Mortier - Cham, Switzerland: Springer, 2022 - Springer
Back in the summer of 2019, we were considering the maintenance of Owl's documentation.
We were glad that documentation was serving us well and growing day by day. Then it …