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Global Convolutional Self-Action Module for Fast Brain Tumor Image Segmentation
Wei An Yang
, Devin Lautan
, Tong Wei Weng
, Wan Chun Lin
, Yamin Kao
,
Chien Chang Chen
認知智慧與精準健康照護研究中心
生醫科學與工程學系
研究成果
:
雜誌貢獻
›
期刊論文
›
同行評審
5
引文 斯高帕斯（Scopus）
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深入研究「Global Convolutional Self-Action Module for Fast Brain Tumor Image Segmentation」主題。共同形成了獨特的指紋。
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Keyphrases
BraTS 2020
50%
Combinatorial Effect
25%
Computational Complexity
50%
Computational Cost
25%
Convolution Kernel
25%
Convolutional
100%
Convolutional Neural Network
25%
Data Density
100%
Deep Convolutional Neural Network (deep CNN)
25%
Density Functional
100%
Dice Score
25%
Dimension Fusion
25%
Enhancing Tumor
25%
Fast Data
100%
Feature Map
25%
Global Features
25%
High Efficiency
25%
Image Input
25%
Long-range Dependence
25%
Mathematical Properties
25%
Model Accuracy
25%
MRI Brain Tumor
100%
Nave
25%
Normalization Data
50%
Optimized Features
25%
Peritumoral Edema
25%
Score Normalization
25%
Self-action
100%
Sigmoid Function
25%
Transformed Image
25%
Tumor Core
25%
Tumour Image Segmentation
100%
U-Net
25%
U-Net Model
25%
Whole Tumor
25%
Computer Science
Computational Complexity
100%
Computational Cost
50%
Convolutional Neural Network
50%
Deep Convolutional Neural Networks
50%
Experimental Result
50%
Feature Map
50%
Global Feature
50%
Image Segmentation
100%
Mathematical Property
50%
Model Accuracy
50%
Range Dependency
50%
Score Normalization
50%
Sigmoid Activation Function
50%
Transformed Image
50%
U-Net
50%
Engineering
Computational Complexity
100%
Computational Cost
50%
Convolutional Neural Network
100%
Experimental Result
50%
Input Image
100%
Mathematical Property
50%
Sigmoid Function
50%
Mathematics
Computational Cost
25%
Convolution
25%
Convolutional Neural Network
50%
Density Functional
100%
Dice
25%
Long-Range Dependency
25%
Sigmoid Function
25%