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Developing Novel Weighted Correlation Kernels for Convolutional Neural Networks to Extract Hierarchical Functional Connectivities from fMRI for Disease Diagnosis Read chapter Read first chapter

2018 | OriginalPaper | Chapter

Ensemble of Multi-sized FCNs to Improve White Matter Lesion Segmentation

Authors : Zhewei Wang, Charles D. Smith, Jundong Liu

Published in: Machine Learning in Medical Imaging

Publisher: Springer International Publishing

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Abstract

In this paper, we develop a two-stage neural network solution for the challenging task of white-matter lesion segmentation. To cope with the vast variability in lesion sizes, we sample brain MR scans with patches at three different dimensions and feed them into separate fully convolutional neural networks (FCNs). In the second stage, we process large and small lesion separately, and use ensemble-nets to combine the segmentation results generated from the FCNs. A novel activation function is adopted in the ensemble-nets to improve the segmentation accuracy measured by Dice Similarity Coefficient. Experiments on MICCAI 2017 White Matter Hyperintensities (WMH) Segmentation Challenge data demonstrate that our two-stage-multi-sized FCN approach, as well as the new activation function, are effective in capturing white-matter lesions in MR images.

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Metadata
Title
Ensemble of Multi-sized FCNs to Improve White Matter Lesion Segmentation
Authors
Zhewei Wang
Charles D. Smith
Jundong Liu
Copyright Year
2018
Book
Machine Learning in Medical Imaging

Print ISBN: 978-3-030-00918-2

Electronic ISBN: 978-3-030-00919-9

Copyright Year: 2018

DOI
https://doi.org/10.1007/978-3-030-00919-9_26

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