In this section
Several of our current PhD candidates and fellow researchers at the Data Science Institute have published, or in the proccess of publishing, papers to present their research.
@inproceedings{Chen:2020,
author = {Chen, C and Qin, C and Qiu, H and Ouyang, C and Wang, S and Chen, L and Tarroni, G and Bai, W and Rueckert, D},
title = {Realistic adversarial data augmentation for MR image segmentation},
url = {http://arxiv.org/abs/2006.13322v1},
year = {2020}
}
TY - CPAPER
AB - Neural network-based approaches can achieve high accuracy in various medicalimage segmentation tasks. However, they generally require large labelleddatasets for supervised learning. Acquiring and manually labelling a largemedical dataset is expensive and sometimes impractical due to data sharing andprivacy issues. In this work, we propose an adversarial data augmentationmethod for training neural networks for medical image segmentation. Instead ofgenerating pixel-wise adversarial attacks, our model generates plausible andrealistic signal corruptions, which models the intensity inhomogeneities causedby a common type of artefacts in MR imaging: bias field. The proposed methoddoes not rely on generative networks, and can be used as a plug-in module forgeneral segmentation networks in both supervised and semi-supervised learning.Using cardiac MR imaging we show that such an approach can improve thegeneralization ability and robustness of models as well as provide significantimprovements in low-data scenarios.
AU - Chen,C
AU - Qin,C
AU - Qiu,H
AU - Ouyang,C
AU - Wang,S
AU - Chen,L
AU - Tarroni,G
AU - Bai,W
AU - Rueckert,D
PY - 2020///
TI - Realistic adversarial data augmentation for MR image segmentation
UR - http://arxiv.org/abs/2006.13322v1
UR - http://hdl.handle.net/10044/1/80458
ER -
Data Science Institute
William Penney Laboratory
Imperial College London
South Kensington Campus
London SW7 2AZ
United Kingdom
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