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 …
Deep semi-supervised learning for medical image segmentation: A review
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 …
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
Automatic vertebral body contour extraction (AVBCE) from heterogeneous spinal MRI is
indispensable for the comprehensive diagnosis and treatment of spinal diseases. However …
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
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 …
in natural images, serving as a versatile perceptual tool for various downstream image …
A multi-task network for anatomy identification in endoscopic pituitary surgery
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 …
encase the surrounding critical structures. This, in combination with anatomical variations …
Data efficient deep learning for medical image analysis: A survey
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 …
analysis. However, despite these achievements, the further enhancement of deep learning …
Translation consistent semi-supervised segmentation for 3d medical images
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 …
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
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 …
to leverage unlabeled images in the training process. Most methods maintain consistent …
Contour-aware consistency for semi-supervised medical image segmentation
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 …
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
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 …
success in the field of 3D medical image segmentation. However, most of the existing …