Recently, U-Net architecture with its strong adaptability has become prevalent in the field of MRI brain tumor segmentation. Meanwhile, researchers have demonstrated that introducing attention mechanisms, especially self-attention, into U-Net can effectively improve the perfor...
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...
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 CNN,一个是正常分辨率,另一个是下采样输入,这样可以结合局部和更大的上下文信息。 总结 本文中,我们介绍了医学成像行业在处理3D MRI和CT扫描时使用的...
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 ...
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 ...
实验结果表明,基于多尺度融合3D U-Net感染区域分割网络在肺部图像分割中,对肺背景,左右肺,感染区域的平均Dice系数为99.76%,95.54%,77.24%,灵敏度分别为99.79%,96.28%,73.83%,特异性分别为97.82%,99.78%,99.85%.该算法在COVID-19胸部CT图像的自动分割中具有良好的性能.本研究推动了CT图像中COVID-19肺部感染的...
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 ...
In view of the above problems, this paper does not use the pre-trained C3D network model to extract the style features of the 3D model, but adopts a new loss function and introduces a 3D U-Net network model with an attention mechanism to better complement model details lost during PCA di...
The 3D attention U-Net network was applied to a complex river channel sandstone reservoir to test its effects. The results show that compared with CNN-PCA method, the 3D attention U-Net network could better complement the details of geological model lost in the PCA...
[1] LiDAR-based Online 3D Video Object Detection with Graph-based Message Passing and Spatiotemporal Transformer Attention. : https://arxiv.org/pdf/2004.01389 [2]nuscenes: A multimodal dataset for autonomous driving : https://arxiv.org/abs/1903.11027 ...