Channel Fusion(渠道融合)研究综述
Channel Fusion 渠道融合 - NEW METHODS First, electroencephalogram (EEG) time series were converted into two-dimensional images for multichannel fusion. [1]新方法 首先，将脑电图（EEG）时间序列转换为二维图像进行多通道融合。 [1]
convolutional neural network 卷积神经网络
Secondly, improve the convolutional neural network through dual-channel fusion, multi-layer convolution and pooling, and combine the batch normalization method in the convolutional layer to separately detect short circuit faults in the line feature extraction is performed on the false alarm data, and then classified and identified through the soft-max classifier to construct an intelligent and efficient fault identification model, which effectively reduces the false alarm rate. [1] The specific aim of the study is to develop a multi-channel fusion convolutional neural network (MCF-CNN) including two branches of CNNs and a trunk merged by an intermediate fusion layer to obtain trained linguistic features from the raw data and perform the classification. [2] Purpose To evaluate the diagnostic performance of deep learning with a multichannel fusion three-dimensional convolutional neural network (MCF-3DCNN) in the differentiation of the pathologic grades of hepatocellular carcinoma (HCC) based on dynamic contrast-enhanced magnetic resonance images (DCE-MR images). [3] The framework of the proposed model contains three convolutional neural network branches: one multi-channel fusion convolutional neural network branch and two 1-D convolutional neural network branches. [4]其次，通过双通道融合、多层卷积和池化对卷积神经网络进行改进，结合卷积层中的批量归一化方法，对虚警数据进行线路特征提取时分别检测短路故障，并然后通过soft-max分类器进行分类识别，构建智能高效的故障识别模型，有效降低误报率。 [1] 该研究的具体目的是开发一个多通道融合卷积神经网络 (MCF-CNN)，包括两个 CNN 分支和一个由中间融合层合并的主干，以从原始数据中获取训练的语言特征并执行分类。 [2] 目的 评估多通道融合三维卷积神经网络 (MCF-3DCNN) 深度学习在基于动态对比增强磁共振图像 (DCE-MR) 区分肝细胞癌 (HCC) 病理分级中的诊断性能。图片）。 [3] 所提出模型的框架包含三个卷积神经网络分支：一个多通道融合卷积神经网络分支和两个一维卷积神经网络分支。 [4]
Feature Channel Fusion
In this paper, a vestibule segmentation method from CT images has been proposed specifically, which exploits different deep feature fusion strategies, including convolutional feature fusion for different receptive fields, channel attention based feature channel fusion, and encoder-decoder feature fusion. [1] Then, we use the feature channel fusion and a Line Segment Detector (LSD) algorithm to extract the target information from the background, and build the motion model of the beam target. [2]在本文中，专门提出了一种 CT 图像的前庭分割方法，该方法利用了不同的深度特征融合策略，包括针对不同感受野的卷积特征融合、基于通道注意的特征通道融合和编码器-解码器特征融合。 [1] nan [2]
channel fusion convolutional
First, high-quality data generation is ensured through the proposed convolutional recurrent generative adversarial network, which adopts a two-channel fusion convolutional recurrent neural network. [1] The specific aim of the study is to develop a multi-channel fusion convolutional neural network (MCF-CNN) including two branches of CNNs and a trunk merged by an intermediate fusion layer to obtain trained linguistic features from the raw data and perform the classification. [2] The framework of the proposed model contains three convolutional neural network branches: one multi-channel fusion convolutional neural network branch and two 1-D convolutional neural network branches. [3]首先，通过提出的卷积递归生成对抗网络确保高质量的数据生成，该网络采用双通道融合卷积递归神经网络。 [1] 该研究的具体目的是开发一个多通道融合卷积神经网络 (MCF-CNN)，包括两个 CNN 分支和一个由中间融合层合并的主干，以从原始数据中获取训练的语言特征并执行分类。 [2] 所提出模型的框架包含三个卷积神经网络分支：一个多通道融合卷积神经网络分支和两个一维卷积神经网络分支。 [3]
channel fusion module 频道融合模块
First, a channel fusion module allows for effective fusing depth and high-level RGB features. [1] , a multichannel fusion module, an Enhanced Atrous Spatial Pyramid Pooling module, and Space-to-Depth/Depth-to-Space operations, to outperform state-of-the-art DCNN-based water body detection methods. [2]首先，通道融合模块允许有效融合深度和高级 RGB 特征。 [1] 、多通道融合模块、增强的 Atrous 空间金字塔池化模块和 Space-to-Depth/Depth-to-Space 操作，优于最先进的基于 DCNN 的水体检测方法。 [2]