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...
U-Net is well-liked in medical image segmentation, but it doesn't fully explore useful features of the channel and capitalize on the contextual information. Therefore, we present an improved U-Net with residual connections, adding a plug-and-play, very portable channel attention (CA) block ...
1. The model is implemented based on the U-Net structure and consists of three main ideas: a residual block with DropBlock, a MFCA module, and a cascaded refined U-shape design. Next, we will introduce the overall architecture and the three major modules of the model in detail. Datasets ...
In this work, we propose a new deep learning model, namely Channel Attention Residual U-Net (CAR-U-Net), to accurately segment retinal vascular and non-vascular pixels. In this model, the channel attention mechanism was introduced into Residual Block and a Channel Attention Residual Block (...
基于Wave-U-Net的声学回声消除方法:文献提到了一种名为"Wave-U-Net"的方法,用于声学回声消除。这表明作者采用了深度学习的方法来处理声学回声问题,其中Wave-U-Net用于生成估计的近端语音。使用辅助编码器提取特征:文献还提到了辅助编码器,它用于提取远端语音的潜在特征。这种方法可能有助于将混音中的有关远端语音的...
加权作用在空间尺度上,给不同空间区域加权:Residual Attention Network for Image Classification 加权作用在特征通道上,给不同通道特征加权:Squeeze-and-Excitation Networks 加权作用在不同时刻历史特征上,结合循环结构添加权重:聚集在视觉领域的视频处理技术上,与RNN/LSTM/GRU的循环结构相结合 关于更多注意力机制的说明推...
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...
殘差塊: Road Extraction by Deep Residual U-Net 靜脈資料集 靜脈資料集來自https://github.com/wilchesf/dorsalhandveins。可以在 data/membrane 資料夾中看到幾張原圖與自製標籤。 資料集一共有1782張手背靜脈影像(自行提取感興趣區域) 拆成 7:2:1 用於 訓練:驗證:測試 分別將圖像跟對應的標籤放在下述資料...
2. 论文翻译:2020_Attention Wave-U-Net for Acoustic Echo Cancellation(892) 3. 论文翻译:2020_Nonlinear Residual Echo Suppression using a Recurrent Neural Network(866) 4. 论文翻译:2021_Semi-Blind Source Separation for Nonlinear Acoustic Echo Cancellation(817) 5. 论文翻译:2020_The INTERSPEECH 20...
Guo et al. proposed the CRA U-Net in [48]. The channel attention residual U-Net was proposed by [49], and Yang et al. A residual attention model with dual supervision was put forth by [50]. Using a multi-residual attention block (MBA), a densely connected residual network with an ...