Search Results for author:
Found 7 papers, 6 papers with code
Loss Functions in the Era of Semantic Segmentation: A Survey and Outlook
Semantic image segmentation, the process of classifying each pixel in an image into a particular class, plays an important role in many visual understanding systems.
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.
Self-supervised Semantic Segmentation: Consistency over Transformation
Accurate medical image segmentation is of utmost importance for enabling automated clinical decision procedures.
Self-supervised Few-shot Learning for Semantic Segmentation: An Annotation-free Approach
This approach eliminates the need for manual annotation, making it particularly suitable for medical images with limited annotated data.
Ranked #16 on
Few-Shot Semantic Segmentation
on FSS-1000 (5-shot)
Medical Image Segmentation Review: The success of U-Net
U-Net is the most widespread image segmentation architecture due to its flexibility, optimized modular design, and success in all medical image modalities.
TransDeepLab: Convolution-Free Transformer-based DeepLab v3+ for Medical Image Segmentation
Especially, deep neural networks based on seminal architectures such as U-shaped models with skip-connections or atrous convolution with pyramid pooling have been tailored to a wide range of medical image analysis tasks.
