Search

Your search keyword '"Zhun Fan"' showing total 471 results
Start Over Author "Zhun Fan"
471 results on '"Zhun Fan"'

118. Evolution of Controllers Under a Generalized Structure Encoding/Decoding Scheme With Application to Magnetic Levitation System

Author

Wenchao Xue, Jiaoyang Zhan, Zhun Fan, Xin Bin, Jie Chen, Yipeng Wang, and Tao Cai

Subjects
Scheme (programming language), Control and Systems Engineering, Computer science, Encoding (memory), Structure (category theory), Electrical and Electronic Engineering, Topology, Magnetic levitation system, computer, Decoding methods, computer.programming_language
Published
2022
Full Text
View/download PDF

135. Genetic U-Net: Automatically Designed Deep Networks for Retinal Vessel Segmentation Using a Genetic Algorithm

Author

Wenji Li, Liu Jinchao, Guijie Zhu, Xinjian Chen, Yibiao Rong, Jiahong Wei, Jiajie Mo, and Zhun Fan

Subjects
Network architecture, Radiological and Ultrasound Technology, Computational complexity theory, Fundus Oculi, business.industry, Computer science, ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION, Retinal Vessels, Pattern recognition, Overfitting, Convolutional neural network, Computer Science Applications, Retinal vessel, Genetic algorithm, Image Processing, Computer-Assisted, Segmentation, Neural Networks, Computer, Artificial intelligence, Electrical and Electronic Engineering, business, Software
Abstract

Recently, many methods based on hand-designed convolutional neural networks (CNNs) have achieved promising results in automatic retinal vessel segmentation. However, these CNNs remain constrained in capturing retinal vessels in complex fundus images. To improve their segmentation performance, these CNNs tend to have many parameters, which may lead to overfitting and high computational complexity. Moreover, the manual design of competitive CNNs is time-consuming and requires extensive empirical knowledge. Herein, a novel automated design method, called Genetic U-Net, is proposed to generate a U-shaped CNN that can achieve better retinal vessel segmentation but with fewer architecture-based parameters, thereby addressing the above issues. First, we devised a condensed but flexible search space based on a U-shaped encoder-decoder. Then, we used an improved genetic algorithm to identify better-performing architectures in the search space and investigated the possibility of finding a superior network architecture with fewer parameters. The experimental results show that the architecture obtained using the proposed method offered a superior performance with less than 1% of the number of the original U-Net parameters in particular and with significantly fewer parameters than other state-of-the-art models. Furthermore, through in-depth investigation of the experimental results, several effective operations and patterns of networks to generate superior retinal vessel segmentations were identified. The codes of this work are available at https://github.com/96jhwei/Genetic-U-Net.

Published
2022
Full Text
View/download PDF

Catalog

Books, media, physical & digital resources
See catalog results