Semantic segmentation of remote sensing imagery (RSI) is critical in many domains due to the diverse landscapes and different sizes of geo-objects that RSI contains, making semantic segmentation challenging. In this paper, a convolutional network, named
In this paper, we proposed a new 3D medical image segmentation model by adding the Swin Block3D module based on the Swin Transformer and Conv Block3D module based on CNN to each decoder and encoder of the model. The Swin Block3D sub-module based on ViT is responsible for learning the glo...
1. 【Medical Image Segmentation】Automated Segmentation of Cervical Nuclei in Pap Smear Images using Deformable Multi-path Ensemble Model 【医学影像处理】使用可变形多路径集成模型自动分割巴氏涂片图像中的宫颈核 作者:Jie Zhao, Quanzheng Li, Xiang Li, Hongfeng Li, Li Zhang 链接: arxiv.org/abs/1812.00...
Meaning, the first transposed convolutional layer concatenates along the channel axis with the last downsampled block, and so on.The very final layer has the output channels same as the number of classes in the dataset. Also, the final layer is a 2D convolution as it is a segmentation ...
At present, cross entropy loss function has been widely used in semantic segmentation network models. When the cloud and snow is far less than the number of pixels in remote sensing image background pixel number, if you use the cross entropy as the loss function, in the process of network ...
To address these issues, this study uses a residual grouped convolution module, convolutional block attention module, and bilinear interpolation upsampling method to improve the classical segmentation network U-net. The influence of network normalization, loss function, and network depth on segmentation pe...
图10图 2分割结果Fig.10 Segmentation results of image 2 (a)—原图像;(b)—NUR法;(c)—分水岭算法;(d)—UR法. 从图8的P-R曲线图和表 2的性能指标数据中可以看出, UR法曲线完全包住了另外两条曲线, 所以UR法的泛化性能更好, 且分割准确率和精确率更高. ...
nnU-Net is the first segmentation method that is designed to deal with the dataset diversity found in the somain. It condenses and automates the keys decisions for designing a successful segmentation pipeline for any given dataset. nnU-Net makes the following contributions to the field: ...
Figure 2. Block diagram of proposed framework for COVID-19 CT segmentation. The section is organized as follows: Section 3.1 is the CT preprocessing stage. Section 3.2 is the SAA-UNet model architecture’s description with the details of the spatial attention module (SAM) in Section 3.3 and ...
calcified plaque segmentation; attention mechanism; low rank; CNN; gating mechanism1. Introduction Coronary artery disease (CAD) has become one of the leading causes of human mortality [1] and accounts for the highest proportion of all cardiac deaths. It is characterized by sudden onset and high...