Is texture predictive for age and sex in brain MRI?
File(s)
Working paper
Author(s)
Pawlowski, Nick
Glocker, Ben
Type
Working Paper
Abstract
Deep learning builds the foundation for many medical image analysis tasks
where neuralnetworks are often designed to have a large receptive field to
incorporate long spatialdependencies. Recent work has shown that large
receptive fields are not always necessaryfor computer vision tasks on natural
images. We explore whether this translates to certainmedical imaging tasks such
as age and sex prediction from a T1-weighted brain MRI scans.
where neuralnetworks are often designed to have a large receptive field to
incorporate long spatialdependencies. Recent work has shown that large
receptive fields are not always necessaryfor computer vision tasks on natural
images. We explore whether this translates to certainmedical imaging tasks such
as age and sex prediction from a T1-weighted brain MRI scans.
Date Issued
2019-07-25
Citation
2019
Publisher
arXiv
Copyright Statement
©2019 The Author(s).
Identifier
http://arxiv.org/abs/1907.10961v1
Subjects
eess.IV
eess.IV
cs.CV
Notes
MIDL 2019 [arXiv:1907.08612]
Publication Status
Published