Learning with limited annotations: a survey on deep semi-supervised learning for medical image segmentation
Medical image segmentation is a fundamental and critical step in many image-guided
clinical approaches. Recent success of deep learning-based segmentation methods usually …
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 …
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
Convolutional neural network (CNN) has shown dissuasive accomplishment on different
areas especially Object Detection, Segmentation, Reconstruction (2D and 3D), Information …
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 …
Semi-supervised learning has been widely applied to medical image segmentation tasks …
MedShapeNet--A large-scale dataset of 3D medical shapes for computer vision
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 …
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
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 …
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
Segment Anything Model (SAM) has achieved impressive results for natural image
segmentation with input prompts such as points and bounding boxes. Its success largely …
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
Automatic intracranial hemorrhage segmentation in 3D non-contrast head CT (NCCT) scans
is significant in clinical practice. Existing hemorrhage segmentation methods usually ignores …
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
Late gadolinium enhancement (LGE) is a specialized imaging technique used in
cardiovascular magnetic resonance (CMR) imaging to detect and characterize areas of scar …
cardiovascular magnetic resonance (CMR) imaging to detect and characterize areas of scar …
Segment anything model with uncertainty rectification for auto-prompting medical image segmentation
The introduction of the Segment Anything Model (SAM) has marked a significant
advancement in prompt-driven image segmentation. However, SAM's application to medical …
advancement in prompt-driven image segmentation. However, SAM's application to medical …