Cross prompting consistency with segment anything model for semi-supervised medical image segmentation
Semi-supervised learning (SSL) has achieved notable progress in medical image
segmentation. To achieve effective SSL, a model needs to be able to efficiently learn from …
segmentation. To achieve effective SSL, a model needs to be able to efficiently learn from …
Dual consistency regularization with subjective logic for semi-supervised medical image segmentation
Semi-supervised learning plays a vital role in computer vision tasks, particularly in medical
image analysis. It significantly reduces the time and cost involved in labeling data. Current …
image analysis. It significantly reduces the time and cost involved in labeling data. Current …
Multi-consistency for semi-supervised medical image segmentation via diffusion models
Y Chen, Y Liu, M Lu, L Fu, F Yang - Pattern Recognition, 2025 - Elsevier
Medical image segmentation presents a formidable challenge, compounded by the scarcity
of annotated data in numerous datasets. Semi-supervised methods offer viable solutions to …
of annotated data in numerous datasets. Semi-supervised methods offer viable solutions to …
Dynamic graph consistency and self-contrast learning for semi-supervised medical image segmentation
G Li, J Xie, L Zhang, G Cheng, K Zhang, M Bai - Neural Networks, 2024 - Elsevier
Semi-supervised medical image segmentation endeavors to exploit a limited set of labeled
data in conjunction with a substantial corpus of unlabeled data, with the aim of training …
data in conjunction with a substantial corpus of unlabeled data, with the aim of training …
TLF: Triple learning framework for intracranial aneurysms segmentation from unreliable labeled CTA scans
L Chai, S Xue, D Tang, J Liu, N Sun, X Liu - Computerized Medical Imaging …, 2024 - Elsevier
Intracranial aneurysm (IA) is a prevalent disease that poses a significant threat to human
health. The use of computed tomography angiography (CTA) as a diagnostic tool for IAs …
health. The use of computed tomography angiography (CTA) as a diagnostic tool for IAs …
MMSeg: A novel multi-task learning framework for class imbalance and label scarcity in medical image segmentation
Semi-supervised medical image segmentation can effectively alleviate the high costs
associated with obtaining high-quality labels and issues related to data privacy. However …
associated with obtaining high-quality labels and issues related to data privacy. However …
Advancing MRI segmentation with CLIP-driven semi-supervised learning and semantic alignment
Precise segmentation and reconstruction of multi-structures within MRI are crucial for clinical
applications such as surgical navigation. However, medical image segmentation faces …
applications such as surgical navigation. However, medical image segmentation faces …
Exploiting Scale-Variant Attention for Segmenting Small Medical Objects
Early detection and accurate diagnosis can predict the risk of malignant disease
transformation, thereby increasing the probability of effective treatment. Identifying mild …
transformation, thereby increasing the probability of effective treatment. Identifying mild …
Neighbor Does Matter: Density-Aware Contrastive Learning for Medical Semi-supervised Segmentation
In medical image analysis, multi-organ semi-supervised segmentation faces challenges
such as insufficient labels and low contrast in soft tissues. To address these issues, existing …
such as insufficient labels and low contrast in soft tissues. To address these issues, existing …
Leveraging Fixed and Dynamic Pseudo-labels for Semi-supervised Medical Image Segmentation
S Kumari, P Singh - arXiv preprint arXiv:2405.07256, 2024 - arxiv.org
Semi-supervised medical image segmentation has gained growing interest due to its ability
to utilize unannotated data. The current state-of-the-art methods mostly rely on pseudo …
to utilize unannotated data. The current state-of-the-art methods mostly rely on pseudo …