Parallel multi‐scale network with attention mechanism for pancreas segmentation

2021 ◽  
Author(s):  
Jianwu Long ◽  
Xinlei Song ◽  
Yong An ◽  
Tong Li ◽  
Jiangzhou Zhu
Keyword(s):  
Attention Mechanism ◽  
Multi Scale ◽  
Scale Network ◽  
Pancreas Segmentation

An adaptive multi-scale network with nonorthogonal multi-union input for reducing false positive of lymph nodes

2021 ◽  
Vol 41 (1) ◽  
pp. 265-277
Author(s):  
Xingqi Meng ◽  
Yanjun Peng ◽  
Yanfei Guo
Keyword(s):  
Lymph Nodes ◽  
False Positive ◽  
Multi Scale ◽  
Scale Network

Wide Weighted Attention Multi-Scale Network for Accurate MR Image Super-Resolution

2021 ◽  
pp. 1-1
Author(s):  
Haoqian Wang ◽  
Xiaowan Hu ◽  
Xiaole Zhao ◽  
Yulun Zhang
Keyword(s):  
Super Resolution ◽  
Mr Image ◽  
Multi Scale ◽  
Scale Network ◽  
Image Super Resolution

Deep learning-based tool wear prediction and its application for machining process using multi-scale feature fusion and channel attention mechanism

Measurement ◽  
2021 ◽  
Vol 177 ◽  
pp. 109254
Author(s):  
Xingwei Xu ◽  
Jianwen Wang ◽  
Bingfu Zhong ◽  
Weiwei Ming ◽  
Ming Chen
Keyword(s):  
Deep Learning ◽  
Tool Wear ◽  
Feature Fusion ◽  
Attention Mechanism ◽  
Machining Process ◽  
Wear Prediction ◽  
Scale Feature ◽  
Multi Scale ◽  
Tool Wear Prediction

Attention-based Spatial-temporal Multi-scale Network for Face Anti-spoofing

2021 ◽  
pp. 1-1
Author(s):  
Wei Zheng ◽  
Mengyuan Yue ◽  
Shuhuan Zhao ◽  
Shuaiqi Liu
Keyword(s):  
Multi Scale ◽  
Scale Network

scholarly journals Non-Intrusive Load Disaggregation Based on a Multi-Scale Attention Residual Network

2020 ◽  
Vol 10 (24) ◽  
pp. 9132
Author(s):  
Liguo Weng ◽  
Xiaodong Zhang ◽  
Junhao Qian ◽  
Min Xia ◽  
Yiqing Xu ◽  
...  
Keyword(s):  
Smart Grids ◽  
Recognition Rate ◽  
Low Frequency ◽  
Attention Mechanism ◽  
Learning Ability ◽  
Residual Network ◽  
Multi Scale ◽  
Energy Disaggregation ◽  
Benchmark Datasets ◽  
Load Disaggregation

Non-intrusive load disaggregation (NILD) is of great significance to the development of smart grids. Current energy disaggregation methods extract features from sequences, and this process easily leads to a loss of load features and difficulties in detecting, resulting in a low recognition rate of low-use electrical appliances. To solve this problem, a non-intrusive sequential energy disaggregation method based on a multi-scale attention residual network is proposed. Multi-scale convolutions are used to learn features, and the attention mechanism is used to enhance the learning ability of load features. The residual learning further improves the performance of the algorithm, avoids network degradation, and improves the precision of load decomposition. The experimental results on two benchmark datasets show that the proposed algorithm has more advantages than the existing algorithms in terms of load disaggregation accuracy and judgments of the on/off state, and the attention mechanism can further improve the disaggregation accuracy of low-frequency electrical appliances.

Multi-Scale Network Based on Dilated Convolution for Bladder Tumor Segmentation of Two-Dimensional MRI Images

2020 ◽  
Author(s):  
Jinyang Xu ◽  
Li Kang ◽  
Wenzhong Han ◽  
Jinwan Jiang ◽  
Ziqi Zhou ◽  
...  
Keyword(s):  
Bladder Tumor ◽  
Tumor Segmentation ◽  
Two Dimensional ◽  
Multi Scale ◽  
Dilated Convolution ◽  
Scale Network
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