Cascaded deep convolutional encoder-decoder neural networks for efficient liver tumor segmentation 截至3.8,引用次数22 这篇文章把CT腹部扫描图分割当成一个分类问题处理,使用一个基于CNN的级连分类器框架。使用两个编码解码器卷积网络训练来进行级联分割肝和病灶(EDCNN)。即第一个EDCNN分割肝图片的结果(ROI区域)作...
标题放不下了,论文全称:Image Restoration Using Very Deep Convolutional Encoder-Decoder Networks with Symmetric Skip Connections 其实就是conv和deconv,外加对称的skip connection。作者表示每两层就会有一个skip connection。 作者的意思是说,conv的作用就是进行feature extraction,保留图中对象的主要组件,同时消除corru...
decode网络中的decoder 利用对应encoder feature map中保存的max-index对输入的feature map进行上采样,产生的稀疏feature maps后接一系列可训练的卷积核,输出密集的feature maps,后接BN用于规范化处理正则化减弱过拟合,与输入对应的decoder产生多通道feature map,虽然输入只有(RGB)三通道。其他的encoder,decoder的通道数,...
41笔记 摘要原文 We present a novel and practical deep fully convolutional neural network architecture for semantic pixel-wise segmentation termed SegNet. This core trainable segmentation engine consists of an encoder network, a corresponding decoder network followed by a pixel-wise classification layer. ...
PixISegNet: pixel-level iris segmentation network using convolutional encoder–decoder with stacked hourglass bottleneck In this paper, we present a new iris ROI segmentation algorithm using a deep convolutional neural network (NN) to achieve the state-of-the-art segmentation... RR Jha,G Jaswal,D...
OverSegNet: A convolutional encoder-decoder network for image over-segmentation Efficient and differentiable image over-segmentation is key to superpixel-based research and applications but remains a challenging problem. The paper prop... P Li,W Ma - 《Computers & Electrical Engineering》 被引量: 0...
Yang, “Image restoration using very deep convolutional encoder-decoder networks with symmetric skip connections,” in Advances in neural information processing systems, 2016, pp. 2802–2810.[147] T. Mikolov, M. Karafiát, L. Burget, J. Černock\`y, and S. Khudanpur, “Recurrent neural ...
Bayesian deep convolutional encoder-decoder networks for surrogate modeling and uncertainty quantification - cics-nd/cnn-surrogate
The decoder unit uses a segmentation map Gmic,∅s, where ∅s is the segmentation network parameter. Later, some convolutional layers are used in encoder unit to increase the dimension of each layer. In this regard, the two-dimensional matrix of the image and the mask enter the network as...
First, two image segmentation architectures, U-Net and SegNet, that share similar encoder and decoder network architectures, except for some differences, were implemented. SegNet uses the basic architecture from VGGNet20 with the pre-trained convolutional layer and batch normalization, while its decoder...