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Authors: Samya Amiri 1 ; Mohamed Ali Mahjoub 1 and Islem Rekik 2

Affiliations: 1 University of Sousse, Tunisia ; 2 University of Dundee, United Kingdom

ISBN: 978-989-758-275-2

Keyword(s): Structured Random Forest, Bayesian Network, Deep Cooperative Network, Autocontext Model, Brain Tumor Segmentation, MRIs.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Artificial Intelligence and Decision Support Systems ; Bayesian Networks ; Biomedical Engineering ; Biomedical Signal Processing ; Data Manipulation ; Enterprise Information Systems ; Health Engineering and Technology Applications ; Human-Computer Interaction ; Methodologies and Methods ; Neurocomputing ; Neurotechnology, Electronics and Informatics ; Pattern Recognition ; Physiological Computing Systems ; Sensor Networks ; Soft Computing

Abstract: Brain cancer phenotyping and treatment is highly informed by radiomic analyses of medical images. Specifically, the reliability of radiomics, which refers to extracting features from the tumor image intensity, shape and texture, depends on the accuracy of the tumor boundary segmentation. Hence, developing fully-automated brain tumor segmentation methods is highly desired for processing large imaging datasets. In this work, we propose a cooperative learning framework for multi-label brain tumor segmentation, which leverages on Structured Random Forest (SRF) and Bayesian Networks (BN). Basically, we embed both strong SRF and BN classifiers into a multi-layer deep architecture, where they cooperate to better learn tumor features for our multi-label classification task. The proposed SRF-BN cooperative learning integrates two complementary merits of both classifiers. While, SRF exploits structural and contextual image information to perform classification at the pixel-level, BN re presents the statistical dependencies between image components at the superpixel-level. To further improve this SRF-BN cooperative learning, we ‘deepen’ this cooperation through proposing a multi-layer framework, wherein each layer, BN inputs the original multi-modal MR images along with the probability maps generated by SRF. Through transfer learning from SRF to BN, the performance of BN improves. In turn, in the next layer, SRF will also benefit from the learning of BN through inputting the BN segmentation maps along with the original multimodal images. With the exception of the first layer, both classifiers use the output segmentation maps resulting from the previous layer, in the spirit of auto-context models. We evaluated our framework on 50 subjects with multimodal MR images (FLAIR, T1, T1-c) to segment the whole tumor, its core and enhanced tumor. Our segmentation results outperformed those of several comparison methods, including the independent (non-cooperative) learning of SRF and BN. (More)

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Paper citation in several formats:
Amiri S., Mahjoub M. and Rekik I. (2018). Bayesian Network and Structured Random Forest Cooperative Deep Learning for Automatic Multi-label Brain Tumor Segmentation.In Proceedings of the 10th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART, ISBN 978-989-758-275-2, pages 183-190. DOI: 10.5220/0006629901830190

@conference{icaart18,
author={Samya Amiri and Mohamed Ali Mahjoub and Islem Rekik},
title={Bayesian Network and Structured Random Forest Cooperative Deep Learning for Automatic Multi-label Brain Tumor Segmentation},
booktitle={Proceedings of the 10th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,},
year={2018},
pages={183-190},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006629901830190},
isbn={978-989-758-275-2},
}

TY - CONF

JO - Proceedings of the 10th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,
TI - Bayesian Network and Structured Random Forest Cooperative Deep Learning for Automatic Multi-label Brain Tumor Segmentation
SN - 978-989-758-275-2
AU - Amiri S.
AU - Mahjoub M.
AU - Rekik I.
PY - 2018
SP - 183
EP - 190
DO - 10.5220/0006629901830190

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