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Found 11 papers, 6 papers with code
A Probabilistic Hadamard U-Net for MRI Bias Field Correction
Random samples drawn from latent space are then incorporated with a prototypical corrected image to generate multiple plausible images.
CT Liver Segmentation via PVT-based Encoding and Refined Decoding
Accurate liver segmentation from CT scans is essential for effective diagnosis and treatment planning.
Ranked #5 on
Liver Segmentation
on LiTS2017
Adaptive Smooth Activation for Improved Disease Diagnosis and Organ Segmentation from Radiology Scans
We apply this new activation function to two important and commonly used general tasks in medical image analysis: automatic disease diagnosis and organ segmentation in CT and MRI.
FuseNet: Self-Supervised Dual-Path Network for Medical Image Segmentation
Semantic segmentation, a crucial task in computer vision, often relies on labor-intensive and costly annotated datasets for training.
Leveraging Unlabeled Data for 3D Medical Image Segmentation through Self-Supervised Contrastive Learning
Current 3D semi-supervised segmentation methods face significant challenges such as limited consideration of contextual information and the inability to generate reliable pseudo-labels for effective unsupervised data use.
HCA-Net: Hierarchical Context Attention Network for Intervertebral Disc Semantic Labeling
In addition, we introduce a skeletal loss term to reinforce the model's geometric dependence on the spine.
A multi-institutional pediatric dataset of clinical radiology MRIs by the Children's Brain Tumor Network
Pediatric brain and spinal cancers remain the leading cause of cancer-related death in children.
Beyond Self-Attention: Deformable Large Kernel Attention for Medical Image Segmentation
To address these challenges, we introduce the concept of \textbf{Deformable Large Kernel Attention (D-LKA Attention)}, a streamlined attention mechanism employing large convolution kernels to fully appreciate volumetric context.
Self-supervised Semantic Segmentation: Consistency over Transformation
Accurate medical image segmentation is of utmost importance for enabling automated clinical decision procedures.
Laplacian-Former: Overcoming the Limitations of Vision Transformers in Local Texture Detection
Vision Transformer (ViT) models have demonstrated a breakthrough in a wide range of computer vision tasks.
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.
