RAUNet: Residual Attention U-Net for Semantic Segmentation of Cataract Surgical InstrumentsAttentionSemantic segmentationCataractSurgical instrumentSemantic segmentation of surgical instruments plays a crucial role in robot-assisted surgery. However, accurate segmentation of surgical instruments remains a challenge...
U-Net由一个包含多个卷积层的收缩路径组成,用于对输入图像进行下采样,一个扩展路径用于对深层特征图进行上采样,还有一个跳转连接用于合并编码器-解码器网络中的裁剪特征图,在很大程度上提高了医学图像的分割性能。如今,U-Net已经成为解决脑瘤分割任务的一个里程碑。同时,各种改进的U-Net方法,如ResU-Net[15]和Ensem...
Cascaded residual attention U-Net (CRAU-Net) inserts DropBlock into the traditional residual block to avoid overfitting and treats the channels of low-level and high-level features equally in the decoder to stress the shallow features (Dong et al., 2022). Meanwhile, some researchers utilize ...
本文提出 residual feedback,强化图像本质属性的差异,不只局限于几个特定的图像属性。 根据[9],设计了一个简单有效的attention机制,加在residual feedback,给输入的辨识度高的特征分配更多注意力。attention机制采用带有sigmoid 激活函数的简单 gating 门控机制,学习有辨识度的特征通道之间的非线性相互作用,避免特征信息...
Chinese introduction:https://blog.csdn.net/big_dreamer1/article/details/101228624 Note: The size of the input image should be divisible by 32. Citation If you find RAUNet useful in your research, please consider citing: @inproceedings{ni2019raunet, title={RAUNet: Residual attention U-Net f...
CRAUNet: A cascaded residual attention U-Net for retinal vessel segmentation 2022, Computers in Biology and Medicine Citation Excerpt : In order to better identify thin vessels, Zhang et al. [40] aggregated multi-scale information by using the attention mechanism and obtained the multi-output thro...
We propose RMAU-Net to augment the ability of feature representation on the segmentation tasks.We develop a multi-scale attention mechanism to solve the multi-scale problem.We propose a loss function to solve the class imbalance and poor segmentation of difficult samples.We conduct extensive experi...
specific image attributes. Furthermore, we design a simple and effective attention mechanism, which take advantage of ideas of Hu et al. [9], and then we add it on the residual feedback to pay more attention to the discriminative features of input information. In this attention mechanism,...
{ys014, xtang}@ie.cuhk.edu.hk, xgwang@ee.cuhk.edu.hk, 4zhhg@bupt.edu.cn Abstract In this work, we propose "Residual Attention Network", a convolutional neural network using attention mechanism which can incorporate with state-of-art feed forward net- work architecture in an end-to-end...
et al. RAU-Net: U-Net network based on residual multi-scale fusion and attention skip layer for overall spine segmentation. Machine Vision and Applications 34, 10 (2023). https://doi.org/10.1007/s00138-022-01360-4 Download citation Received18 December 2021 Revised27 October 2022 Accepted22 ...