The Attention Multi-scale Sensing Module (AMSM) is designed to localize WBCs more accurately by fusing features at different scales and enhancing feature representation through a self-attention mechanism. The C
The output of 1 × 1 × 1 convolution is used as the weight of the MCA input. We call the entire convolution module FMCA, which is described as follows: (1)F1=Dw_Conv(F)(2)Weight=Conv(F1+∑i=01Scalei(Dw_Conv(F1)))(3)Output=Weight⊗F1 where F represents the input feature of...
This also helps to efficiently increase the RF size of the convolution kernels. As can be seen, the RF size of the kernels in the convolution layers of the MB gradually increases as the training continues. The CMSFL module represents such an efficient approach to increasing RF size by applyin...
To overcome these two problems, we propose a multiscale double-channel convolution U-Net (MDCC-Net) framework for colorectal tumor segmentation. Methods In the coding layer, we designed a dual-channel separation and convolution module and then added residual connections to perform multiscale feature...
The model consists of preprocessing layer, multiscale convolution layer, residual module, global average pooling layer, and full connection layer. In the preprocessing layer, if the model uses the convolution kernel after random initialization to extract image features, then what the model learns will...
The none-dilation model was designed to test the role of the multiscale convolution module. This variant of MBCNN only implemented standard convolution instead of multiscale convolution. Finally, the none-dilation-backcast variant lacks both the backcast branch and the multiscale convolutional layer...
The full network architecture includes four parts: the backbone network, the multiscale dilated convolution module (MDCM), the feature enhancement module (FEM), and the decoder part. For the backbone network, ResNet-101 is utilized. We fed the feature output from the backbone network into the ...
DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution--阅读笔记 特征响应的简单双线性插值。 挑战2的解决方案: 一个标准的处理方法就是将图像转换成图像,然后聚集特征或分数图。作者提出一个由空间金字塔池(spatialpyramid pooling)衍生的方案:在...越来越多的数据表达。不变性意味着...
MSF defines two types of convolution kernels, whose sizes belong to a set K = {1,3}; k1∈ K and k2∈ K are scales of a convolution kernel in longitudinal and transverse, respectively. In Figure 3(a), in order to ensure that the global information of the image is retained, 1 ...
a cross-domain attention module based on convolution and vision Transformer is proposed to capturing long-range dependencies between in-depth features and effectively solves the problem of global context loss. Finally, a nest-connection based decoder is used for implementing feature reconstruction. In ...