Brain Image Segmentation
17 papers with code • 6 benchmarks • 1 datasets
Benchmarks
These leaderboards are used to track progress in Brain Image Segmentation
| Trend | Dataset | Best Model | Paper | Code | Compare |
|---|---|---|---|---|---|
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
CREMI
Latest papers with no code
Deep Learning-Based Brain Image Segmentation for Automated Tumour Detection
Introduction: The present study on the development and evaluation of an automated brain tumor segmentation technique based on deep learning using the 3D U-Net model.
Transferring Ultrahigh-Field Representations for Intensity-Guided Brain Segmentation of Low-Field Magnetic Resonance Imaging
Specifically, our adaptive fusion module aggregates 7T-like features derived from the LF image by a pre-trained network and then refines them to be effectively assimilable UHF guidance into LF image features.
The ASNR-MICCAI Brain Tumor Segmentation (BraTS) Challenge 2023: Intracranial Meningioma
Meningiomas are the most common primary intracranial tumor in adults and can be associated with significant morbidity and mortality.
Finding the Most Transferable Tasks for Brain Image Segmentation
Furthermore, within the same modality, transferring from the source task that has stronger RoI shape similarity with the target task can significantly improve the final transfer performance.
Automatic quality control framework for more reliable integration of machine learning-based image segmentation into medical workflows
Machine learning algorithms underpin modern diagnostic-aiding software, which has proved valuable in clinical practice, particularly in radiology.
Multi-Task Neural Processes
Our multi-task neural processes methodologically expand the scope of vanilla neural processes and provide a new way of exploring task relatedness in function spaces for multi-task learning.
EGMM: an Evidential Version of the Gaussian Mixture Model for Clustering
The Gaussian mixture model (GMM) provides a simple yet principled framework for clustering, with properties suitable for statistical inference.
Transfer Learning from Partial Annotations for Whole Brain Segmentation
Brain MR image segmentation is a key task in neuroimaging studies.
Brain Segmentation from k-space with End-to-end Recurrent Attention Network
The task of medical image segmentation commonly involves an image reconstruction step to convert acquired raw data to images before any analysis.
Automatic Brain Structures Segmentation Using Deep Residual Dilated U-Net
Brain image segmentation is used for visualizing and quantifying anatomical structures of the brain.
