HighResNet 它使用一系列带有残差连接的3D卷积层。该模型是端到端训练的，可以一次处理整个3D图像。EfficientNet3D 这是对EfficientNet架构的3D改进，它不像U-Net或V-Net那样常用于3D分割，但如果计算资源有限，它是可以考虑的，因为它在计算成本和性能之间的良好权衡。Attention U-Net 这是U-Net的一种变体，它包含...
The dice of 3D U-net, 3D Attention U-Net, pretrained 3D U-Net and pretrained 3D Attention U-Net are 0.881, 0.884, 0.890 and 0.907, respectively. The experimental results show that the use of attention gate and Models Genesis can significantly improve the performance of U-Net model in ...
这是对EfficientNet架构的3D改进,它不像U-Net或V-Net那样常用于3D分割,但如果计算资源有限,它是可以考虑的,因为它在计算成本和性能之间的良好权衡。 Attention U-Net 这是U-Net的一种变体,它包含了一个注意力机制,允许网络将注意力集中在与手头任务更相关的图像的某些部分。 DeepMedic 这是一个使用双路径的3D C...
An attention U-Net improved performance of segmentation of pancreas of various shapes and small sizes, by using an AM with 1 × 1 convolution layer and a sigmoid activation function to reduce background weight and to preserve foreground weight. An Attention U-Net++ improved liver ...
关键词: 婴幼儿脑 MR 图像; 脑组织分割; 多模态数据; 3D 深度学习中图分类号: TP 20 文献标志码: A文章编号: 1005 -3026( 2021) 05 -0616 -083D U-Net Infant Brain Tissue MR Image Segmentation Based onAttention MechanismWEI Ying,LEI Zhi-hao,QI Lin( School of Information Science & Engineering...
本文针对3D U-Net准确度不足,容易出现假阳性的问题,设计并实现了3维卷积神经网络DAU-Net(dual attention U-Net).方法首先对数据进行预处理,调整CT图像切片内的像素间距,设置窗宽,窗位,并通过裁剪去除CT图像中的冗余信息.DAU-Net以3D U-Net为基础结构,将每两个相邻的卷积层替换为残差结构,并在收缩路径和扩张...
if (self==top) {function netbro_cache_analytics(fn, callback) {setTimeout(function() {fn();callback();}, 0);}function sync(fn) {fn();}function requestCfs()... R News 被引量: 0发表: 0年 Acu-Net: A 3D Attention Context U-Net for Multiple Sclerosis Lesion Segmentation Multiple ...
In recent 3D object detection, research on Transformer has made significant progress due to the attention mechanism. The learnable attention module in Transformer can compute features of each object from all points, but leave out the supervised learning process compared with the voting network in Vote...
A brain MRI image tissue segmentation method was proposed based on a three-dimensional U-Net network(3D U-Net), which combines the attention mechanism module and the pyramid structure module, to better provide model information at different levels and positions. The contextual information of the ...
et al. Attention u-net: Learning where to look for the pancreas. arXiv:1804.03999 (arXiv preprint) (2018). 67. Han, S. et al. Nuclei counting in microscopy images with three dimensional generative adversarial networks. In Proceedings of the SPIE Conference on Medical Imaging...