Simon Fraser University

U-Net: Convolutional Networks for Biomedical Image Segmentation

In this paper, the authors proposed a fully convolutional neural network architecture for biomedical image segmentation which overcame the limitations of the contemporary algorithms. Unlike other ...
EurekAlert!

Advanced U-net segmentation model using residual grouped convolution and attention mechanism for brain tumor MRI image segmentation

Through a comprehensive evaluation of model complexity and number of parameters, it was determined that the overall performance of the proposed model is the best when eight group convolutions are used ...
EurekAlert!

Medical image segmentation using multi-head self-attention-based residual double u-net

The MHSAttResDU-Net incorporates RCC for complexity control and improved generalization under varying lighting. The SSRP unit in encoder-decoder blocks reduces feature map dimensions, capturing key ...
News Medical

Advancements in MRI brain tumor segmentation with improved U-net model

The brain is responsible for the "general command" of human thinking and coordination of the body. Thus, various brain diseases can cause great damage to the human body and nervous system. Brain ...
Simon Fraser University

V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation

This paper proposes an end-to-end trained fully convolutional neural network model to process 3D image volumes. Unlike previous works that processed the input volumes slice-wise or patch-wise, the ...