2. 一些特定的图像内容在基于CNN的方法中很难保留。 解决问题:本文提出一个双编码器U-net(D-unet),包括一个unfixed encoder自动学习image fingerprints,一个fixed encoder提供方向信息。双编码器之后是一个spatial pyramid global-feature extraction module,扩展全局视野。本文方法使用少量训练样本。 网络结构 unfixed e...
(a) The standard pure convolutional (e.g., UNet 1). (b) The cascaded hybrid model of CNNs and Transformer structure (e.g., TransUNet 2). (c) The pure Transformer structure (e.g., Swin-UNet 3). (d) Ours proposed alternate encoder and dual decoder architecture. Full size image ...
Each decoder block in the first DL network performs up-sampling on the given feature map via the transposed convolution, which effectively increases the size of the feature maps. Afterwards, the features maps of the encoder skip-connections are concatenated with the output feature maps. The skip-...
1(c), DAtt-Unet has the same encoder for the infection and lung segmentation tasks that is used by the skip connections, while each task has its own decoder. The goal is to use the encoder to learn high-level features for both tasks. Then, the decoders use these features to segment ...
We propose Dual-Stream Iterative Transformer UNet (DSIT-UNet), a novel framework that combines Iterative Transformer (IT) modules with a dual-stream encoder鈥揹ecoder architecture. Our model incorporates a transformed spatial鈥揾ybrid attention optimization (TSHAO) module to enhance multiscale feature...
在StyleGAN生成主枝的第四层后插入了一个Auxilliary net(一个Unet),来预测entity(配饰、物件等特殊实体)的mask以及含entity的feature输出。如图,若跳过Aux,则G的结构与StyleGAN等同,生出目标域的人脸图;若经过Aux,则生成图会加上圣诞帽这样的entity。 G和Aux都要训。在生图过程中,将参考图通过e4e encoder invert为...
Currently, methods relying on U-Net face challenges in effectively utilizing fine-grained semantic information from input images and bridging the semantic gap between the encoder and decoder. To address these issues, we propose an FMD-UNet dual-decoder U-Net network for COVID-19 infection ...
首先通过投影构建 3D 特征,然后使用经典 3D UNet 处理特征。但是 UNet 中的 3D 卷积有一些限制:首先,它在相对固定的感受野内推理语义,不适用于所有的语义类别。此外,其空间不变性不能很好的处理稀疏和不连续的 3D 特征,这些特征是由 SOTA 的 2D 图像到 3D 转换方法得到的。最后,3D 卷积参数量太大。因此,本文...
At last, we compared DARU-Net with other advanced deep learning methods, including Attention-UNet,23 LEDNet,24 HRNet,25 and MultiResUNet.26 We performed 5-fold cross validation during training, and used the average value as the evaluation of performance. We verified the performance of each ...
SAtUNet: Series Atrous convolution enhanced U-Net for lung nodule segmentation[J]. Int J Imaging Syst Technol. 2024;34(1):e22964. Article Google Scholar Fu J, Liu J, Tian H et al. Dual attention network for scene segmentation[C]. Proceedings of the IEEE/CVF conference on computer ...