Imperial College London
Portrait Picture

Dr Ben Glocker

Faculty of Engineering, Department of Computing

Professor in Machine Learning for Imaging
 
 
 
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Contact

 

+44 (0)20 7594 8334b.glocker Website CV

 
 
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Location

 

377Huxley BuildingSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@unpublished{Korkinof:2018,
author = {Korkinof, D and Rijken, T and O'Neill, M and Yearsley, J and Harvey, H and Glocker, B},
title = {High-resolution mammogram synthesis using progressive generative adversarial networks},
url = {http://hdl.handle.net/10044/1/62944},
year = {2018}
}
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RIS format (EndNote, RefMan)

TY  - UNPB
AB  - The ability to generate synthetic medical images is useful for data augmentation, domain transfer, and out-of-distribution detection. However, generating realistic, high-resolution medical images is challenging, particularly for Full Field Digital Mammograms (FFDM), due to the textural heterogeneity, fine structural details and specific tissue properties. In this paper, we explore the use of progressively trained generative adversarial networks (GANs) to synthesize mammograms, overcoming the underlying instabilities when training such adversarial models. This work is the first to show that generation of realistic synthetic medical images is feasible at up to 1280x1024 pixels, the highest resolution achieved for medical image synthesis, enabling visualizations within standard mammographic hanging protocols. We hope this work can serve as a useful guide and facilitate further research on GANs in the medical imaging domain.
AU  - Korkinof,D
AU  - Rijken,T
AU  - O'Neill,M
AU  - Yearsley,J
AU  - Harvey,H
AU  - Glocker,B
PY  - 2018///
TI  - High-resolution mammogram synthesis using progressive generative adversarial networks
UR  - http://hdl.handle.net/10044/1/62944
ER  -
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