Imperial College London
Alternate

ProfessorDanielRueckert

Faculty of Engineering, Department of Computing

Professor of Visual Information Processing
 
 
 
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Contact

 

+44 (0)20 7594 8333d.rueckert Website

 
 
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Location

 

568Huxley BuildingSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@article{Chen:2019:10.1016/j.media.2019.101539,
author = {Chen, L and Bentley, P and Mori, K and Misawa, K and Fujiwara, M and Rueckert, D},
doi = {10.1016/j.media.2019.101539},
journal = {Medical Image Analysis},
pages = {1--12},
title = {Self-supervised learning for medical image analysis using image context restoration},
url = {http://dx.doi.org/10.1016/j.media.2019.101539},
volume = {58},
year = {2019}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - Machine learning, particularly deep learning has boosted medical image analysis over the past years. Training a good model based on deep learning requires large amount of labelled data. However, it is often difficult to obtain a sufficient number of labelled images for training. In many scenarios the dataset in question consists of more unlabelled images than labelled ones. Therefore, boosting the performance of machine learning models by using unlabelled as well as labelled data is an important but challenging problem. Self-supervised learning presents one possible solution to this problem. However, existing self-supervised learning strategies applicable to medical images cannot result in significant performance improvement. Therefore, they often lead to only marginal improvements. In this paper, we propose a novel self-supervised learning strategy based on context restoration in order to better exploit unlabelled images. The context restoration strategy has three major features: 1) it learns semantic image features; 2) these image features are useful for different types of subsequent image analysis tasks; and 3) its implementation is simple. We validate the context restoration strategy in three common problems in medical imaging: classification, localization, and segmentation. For classification, we apply and test it to scan plane detection in fetal 2D ultrasound images; to localise abdominal organs in CT images; and to segment brain tumours in multi-modal MR images. In all three cases, self-supervised learning based on context restoration learns useful semantic features and lead to improved machine learning models for the above tasks.
AU  - Chen,L
AU  - Bentley,P
AU  - Mori,K
AU  - Misawa,K
AU  - Fujiwara,M
AU  - Rueckert,D
DO  - 10.1016/j.media.2019.101539
EP  - 12
PY  - 2019///
SN  - 1361-8415
SP  - 1
TI  - Self-supervised learning for medical image analysis using image context restoration
T2  - Medical Image Analysis
UR  - http://dx.doi.org/10.1016/j.media.2019.101539
UR  - https://www.ncbi.nlm.nih.gov/pubmed/31374449
UR  - https://www.sciencedirect.com/science/article/pii/S1361841518304699?via%3Dihub
UR  - http://hdl.handle.net/10044/1/80911
VL  - 58
ER  -
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