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 …, 2023 - 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 …

Bridging 2D and 3D segmentation networks for computation-efficient volumetric medical image segmentation: An empirical study of 2.5 D solutions

Y Zhang, Q Liao, L Ding, J Zhang - Computerized Medical Imaging and …, 2022 - Elsevier
Recently, deep convolutional neural networks have achieved great success for medical
image segmentation. However, unlike segmentation of natural images, most medical images …

On the analyses of medical images using traditional machine learning techniques and convolutional neural networks

S Iqbal, A N. Qureshi, J Li, T Mahmood - Archives of Computational …, 2023 - Springer
Convolutional neural network (CNN) has shown dissuasive accomplishment on different
areas especially Object Detection, Segmentation, Reconstruction (2D and 3D), Information …

Uncertainty-guided mutual consistency learning for semi-supervised medical image segmentation

Y Zhang, R Jiao, Q Liao, D Li, J Zhang - Artificial Intelligence in Medicine, 2023 - Elsevier
Medical image segmentation is a fundamental and critical step in many clinical approaches.
Semi-supervised learning has been widely applied to medical image segmentation tasks …

MedShapeNet--A large-scale dataset of 3D medical shapes for computer vision

J Li, Z Zhou, J Yang, A Pepe, C Gsaxner… - arXiv preprint arXiv …, 2023 - arxiv.org
Prior to the deep learning era, shape was commonly used to describe the objects.
Nowadays, state-of-the-art (SOTA) algorithms in medical imaging are predominantly …

[HTML][HTML] Attri-VAE: Attribute-based interpretable representations of medical images with variational autoencoders

I Cetin, M Stephens, O Camara… - … Medical Imaging and …, 2023 - Elsevier
Deep learning (DL) methods where interpretability is intrinsically considered as part of the
model are required to better understand the relationship of clinical and imaging-based …

Sa-med2d-20m dataset: Segment anything in 2d medical imaging with 20 million masks

J Ye, J Cheng, J Chen, Z Deng, T Li, H Wang… - arXiv preprint arXiv …, 2023 - arxiv.org
Segment Anything Model (SAM) has achieved impressive results for natural image
segmentation with input prompts such as points and bounding boxes. Its success largely …

The state-of-the-art 3D anisotropic intracranial hemorrhage segmentation on non-contrast head CT: The INSTANCE challenge

X Li, G Luo, K Wang, H Wang, J Liu, X Liang… - arXiv preprint arXiv …, 2023 - arxiv.org
Automatic intracranial hemorrhage segmentation in 3D non-contrast head CT (NCCT) scans
is significant in clinical practice. Existing hemorrhage segmentation methods usually ignores …

[HTML][HTML] Unsupervised unpaired multiple fusion adaptation aided with self-attention generative adversarial network for scar tissues segmentation framework

A Qayyum, I Razzak, M Mazher, X Lu, SA Niederer - Information Fusion, 2024 - Elsevier
Late gadolinium enhancement (LGE) is a specialized imaging technique used in
cardiovascular magnetic resonance (CMR) imaging to detect and characterize areas of scar …

Segment anything model with uncertainty rectification for auto-prompting medical image segmentation

Y Zhang, S Hu, C Jiang, Y Cheng, Y Qi - arXiv preprint arXiv:2311.10529, 2023 - arxiv.org
The introduction of the Segment Anything Model (SAM) has marked a significant
advancement in prompt-driven image segmentation. However, SAM's application to medical …