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Found 11 papers, 8 papers with code
Enhancing Efficiency in Vision Transformer Networks: Design Techniques and Insights
Intrigued by the inherent ability of the human visual system to identify salient regions in complex scenes, attention mechanisms have been seamlessly integrated into various Computer Vision (CV) tasks.
Vision-Language Synthetic Data Enhances Echocardiography Downstream Tasks
High-quality, large-scale data is essential for robust deep learning models in medical applications, particularly ultrasound image analysis.
SA2-Net: Scale-aware Attention Network for Microscopic Image Segmentation
Specifically, we propose scale-aware attention (SA2) module designed to capture inherent variations in scales and shapes of microscopic regions, such as cells, for accurate segmentation.
DiffGANPaint: Fast Inpainting Using Denoising Diffusion GANs
Free-form image inpainting is the task of reconstructing parts of an image specified by an arbitrary binary mask.
Advances in Medical Image Analysis with Vision Transformers: A Comprehensive Review
The remarkable performance of the Transformer architecture in natural language processing has recently also triggered broad interest in Computer Vision.
Diffusion Models for Medical Image Analysis: A Comprehensive Survey
Then, we provide a systematic taxonomy of diffusion models in the medical domain and propose a multi-perspective categorization based on their application, imaging modality, organ of interest, and algorithms.
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.
TransNorm: Transformer Provides a Strong Spatial Normalization Mechanism for a Deep Segmentation Model
In the past few years, convolutional neural networks (CNNs), particularly U-Net, have been the prevailing technique in the medical image processing era.
HiFormer: Hierarchical Multi-scale Representations Using Transformers for Medical Image Segmentation
In this paper, we propose HiFormer, a novel method that efficiently bridges a CNN and a transformer for medical image segmentation.
Intervertebral Disc Labeling With Learning Shape Information, A Look Once Approach
Accurate and automatic segmentation of intervertebral discs from medical images is a critical task for the assessment of spine-related diseases such as osteoporosis, vertebral fractures, and intervertebral disc herniation.
Contextual Attention Network: Transformer Meets U-Net
Then, they emphasize the informative regions while taking into account the long-range contextual dependency derived by the Transformer module.
