About: CANet: Context Aware Network for Brain Glioma Segmentation

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type	http://eprints.org/ontology/ArticleEPrint http://eprints.org/ontology/EPrint Article Academic Article
seeAlso	HTML Summary of #84603 CANet: Context Aware Network for Brain Glioma Segmentation
sameAs	CANet: Context Aware Network for Brain Glioma Segmentation
http://eprints.org/ontology/hasAccepted	CANet: Context Aware Network for Brain Glioma Segmentation (PDF)
http://eprints.org/ontology/hasDocument	CANet: Context Aware Network for Brain Glioma Segmentation (PDF) CANet: Context Aware Network for Brain Glioma Segmentation (Other) CANet: Context Aware Network for Brain Glioma Segmentation (Other) CANet: Context Aware Network for Brain Glioma Segmentation (Other) CANet: Context Aware Network for Brain Glioma Segmentation (Other) CANet: Context Aware Network for Brain Glioma Segmentation (Other)
dc:hasVersion	CANet: Context Aware Network for Brain Glioma Segmentation (PDF)
Title	CANet: Context Aware Network for Brain Glioma Segmentation
described by	https://demo.openlinksw.com/about/id/entity/https/www.cs.kent.ac.uk/people/staff/akj22/materials/CO644/export_kar_RDFN3.n3
Date	2021-03-15
Creator	Feixiang Zhou Lei Tong Qianni Zhang Zheheng Jiang Yinhai Wang Zhihua Liu Caifeng Shan Huiyu Zhou L. Li Long Chen
status	peer reviewed published
Publisher	IEEE
abstract	Automated segmentation of brain glioma plays an active role in diagnosis decision, progression monitoring and surgery planning. Based on deep neural networks, previous studies have shown promising technologies for brain glioma segmentation. However, these approaches lack powerful strategies to incorporate contextual information of tumor cells and their surrounding, which has been proven as a fundamental cue to deal with local ambiguity. In this work, we propose a novel approach named Context-Aware Network (CANet) for brain glioma segmentation. CANet captures high dimensional and discriminative features with contexts from both the convolutional space and feature interaction graphs. We further propose context guided attentive conditional random fields which can selectively aggregate features. We evaluate our method using publicly accessible brain glioma segmentation datasets BRATS2017, BRATS2018 and BRATS2019. The experimental results show that the proposed algorithm has better or competitive performance against several State-of-The-Art approaches under different segmentation metrics on the training and validation sets.
Is Part Of	IEEE Transactions on Medical Imaging https://kar.kent.ac.uk/id/repository
Subject	Q Science Q Science (General) Q335 Artificial intelligence
list of authors	https://kar.kent.ac.uk/id/eprint/84603#authors
is topic of	https://www.cs.kent.ac.uk/people/staff/akj22/materials/CO644/export_kar_RDFN3.n3
is primary topic of	HTML Summary of #84603 CANet: Context Aware Network for Brain Glioma Segmentation

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