Deep learning has been widely used in med**ical image segmentation and other aspects. However,the performance of existing medical image segmentationmodels has been limited by the challenge of obtainingsufficient high-quality labeled data due to the prohibitivedata annotation cost. To alleviate this lim...
1、We propose LAVT, a Transformer-based referring image segmentation framework that performs languageaware visual encoding in place of cross-modal fusion post feature extraction. 2、We achieve newstate-of-the-artresults on three datasets for referring image segmentation, demonstratingthe effectivenessand ...
Abstract摘要Deep learning has been widely used in med**ical image segmentation and other aspects. However,the performance of existing medical image segmentationmodels has been limited by the challenge of obtainingsufficient high-quality labeled data due to the prohibitivedata annotation cost. To alleviat...
直到Vision Transformer的出现,An Image is Worth 16x16 Words,VIT的出现将纯Transformer结构引入到图像分类中,将图像分块、嵌入以后使用Transformer进行计算,通过MLP来实现分类,并在ImageNet中取得优秀的效果。 VIT的出现启发了语义分割领域,Transformer这种基于Attention的机制,比起CNN需要使用卷积来提升感受野的操作,Attenti...
Recent advances in transformer-based models have drawn attention to exploring these techniques in medical image segmentation, especially in conjunction with the U-Net model (or its variants), which has shown great success in medical image segmentation, under both 2D and 3D settings. Current 2D base...
In this study, we present UTNet, a simple yet powerful hybrid Transformer architecture that integrates self-attention into a convolutional neural network for enhancing medical image segmentation. UTNet applies self-attention modules in both encoder and decoder for capturing long-range dependency at ...
Transformer in ConvNet’s Clothing for Faster Inference, 2021 [2] Guoping Xu, Xingrong Wu, Xuan Zhang, Xinwei He, LeViT-UNet: Make Faster Encoders with Transformer for Medical Image Segmentation, 2021 https://avoid.overfit.cn/post/474870d5912d4cb3aeade0b47c1a97e3 作者：Golnaz Hosseini ...
TransUNet that integrates the advantages of transformer and CNN has achieved success in medical image segmentation tasks. However, TransUNet simply combines feature maps between encoder and decoder via skip connections at the same resolution, which leads to be an unnecessarily restrictive fusion design. ...
Hybrid ViT-Based Medical Image Segmentation Approaches 纯粹的ViT架构,完全依赖于注意力机制,缺乏卷积运算符,可能导致低级细节丢失,从而导致不准确的分割结果。HVTs通过集成ViTs和CNN架构的优势,展示了捕获输入数据中长程和局部上下文的能力。这种独特的组合使HVTs在各种任务上都能实现尖端性能,特别是在医学图像分割方面...
Recently, deep learning with Convolutional Neural Networks (CNNs) and Transformers has shown encouraging results in fully supervised medical image segmentation. However, it is still challenging for them to achieve good performance with limited annotations for training. In this work, we present a very...