ME‐Net : Multi‐encoder net framework for brain tumor segmentation-Reference-Cited by-同舟云学术

ME‐Net : Multi‐encoder net framework for brain tumor segmentation

Published:2021-03-07 Issue:4 Volume:31 Page:1834-1848
ISSN:0899-9457
Container-title:International Journal of Imaging Systems and Technology
language:en
Short-container-title:Int J Imaging Syst Technol

Author:

Zhang Wenbo¹,Yang Guang²³^ORCID,Huang He¹,Yang Weiji⁴,Xu Xiaomei¹,Liu Yongkai⁵,Lai Xiaobo¹

Affiliation:

1. College of Medical Technology Zhejiang Chinese Medical University Hangzhou China

2. Cardiovascular Research Centre Royal Brompton Hospital London UK

3. National Heart and Lung Institute Imperial College London London UK

4. College of Life Science Zhejiang Chinese Medical University Hangzhou China

5. Department of Radiological Sciences, David Geffen School of Medicine University of California at Los Angeles Los Angeles California USA

Funder

British Heart Foundation

H2020 European Research Council

National Natural Science Foundation of China

Publisher

Wiley

Subject

Electrical and Electronic Engineering,Computer Vision and Pattern Recognition,Software,Electronic, Optical and Magnetic Materials

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/ima.22571

Reference32 articles.

1. BakasS ReyesM JakabA et al.Identifying the best machine learning algorithms for brain tumor segmentation progression assessment and overall survival prediction in the BRATS challenge. arXiv preprint arXiv:1811.02629; 2018.

2. Segmentation of glioma tumors in brain using deep convolutional neural network

3. Brain Tumor Segmentation Using Convolutional Neural Networks in MRI Images

4. MyronenkoA.3D MRI brain tumor segmentation using autoencoder regularization.Lecture Notes in Computer Science. Vol 11384;2019.

5. One-Pass Multi-Task Networks With Cross-Task Guided Attention for Brain Tumor Segmentation

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