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 …

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 …

Attractive deep morphology-aware active contour network for vertebral body contour extraction with extensions to heterogeneous and semi-supervised scenarios

S Zhao, J Wang, X Wang, Y Wang, H Zheng… - Medical Image …, 2023 - Elsevier
Automatic vertebral body contour extraction (AVBCE) from heterogeneous spinal MRI is
indispensable for the comprehensive diagnosis and treatment of spinal diseases. However …

SamDSK: Combining segment anything model with domain-specific knowledge for semi-supervised learning in medical image segmentation

Y Zhang, T Zhou, S Wang, Y Wu, P Gu… - arXiv preprint arXiv …, 2023 - arxiv.org
The Segment Anything Model (SAM) exhibits a capability to segment a wide array of objects
in natural images, serving as a versatile perceptual tool for various downstream image …

A multi-task network for anatomy identification in endoscopic pituitary surgery

A Das, DZ Khan, SC Williams, JG Hanrahan… - … conference on medical …, 2023 - Springer
Pituitary tumours are in an anatomically dense region of the body, and often distort or
encase the surrounding critical structures. This, in combination with anatomical variations …

Data efficient deep learning for medical image analysis: A survey

S Kumari, P Singh - arXiv preprint arXiv:2310.06557, 2023 - arxiv.org
The rapid evolution of deep learning has significantly advanced the field of medical image
analysis. However, despite these achievements, the further enhancement of deep learning …

Translation consistent semi-supervised segmentation for 3d medical images

Y Liu, Y Tian, C Wang, Y Chen, F Liu… - … on Medical Imaging, 2024 - ieeexplore.ieee.org
3D medical image segmentation methods have been successful, but their dependence on
large amounts of voxel-level annotated data is a disadvantage that needs to be addressed …

Dual structure-aware image filterings for semi-supervised medical image segmentation

Y Gu, Z Sun, T Chen, X Xiao, Y Liu, Y Xu… - Medical Image Analysis, 2025 - Elsevier
Semi-supervised image segmentation has attracted great attention recently. The key is how
to leverage unlabeled images in the training process. Most methods maintain consistent …

Contour-aware consistency for semi-supervised medical image segmentation

L Li, S Lian, Z Luo, B Wang, S Li - Biomedical Signal Processing and …, 2024 - Elsevier
In medical images, the edges of organs are often blurred and unclear. Existing semi-
supervised image segmentation methods rarely model edges explicitly. Thus most methods …

Semi-Supervised 3D Medical Image Segmentation Using Multi-Consistency Learning With Fuzzy Perception-Guided Target Selection

T Lei, W Song, W Zhang, X Du, C Li… - … on Radiation and …, 2024 - ieeexplore.ieee.org
Semi-supervised learning methods based on the mean teacher model have achieved great
success in the field of 3D medical image segmentation. However, most of the existing …