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@unpublished{Osuala:2021,
author = {Osuala, R and Kushibar, K and Garrucho, L and Linardos, A and Szafranowska, Z and Klein, S and Glocker, B and Diaz, O and Lekadir, K},
publisher = {arXiv},
title = {A review of generative adversarial networks in cancer imaging: new applications, new solutions},
url = {http://arxiv.org/abs/2107.09543v1},
year = {2021}
}
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RIS format (EndNote, RefMan)

TY  - UNPB
AB  - Despite technological and medical advances, the detection, interpretation,and treatment of cancer based on imaging data continue to pose significantchallenges. These include high inter-observer variability, difficulty ofsmall-sized lesion detection, nodule interpretation and malignancydetermination, inter- and intra-tumour heterogeneity, class imbalance,segmentation inaccuracies, and treatment effect uncertainty. The recentadvancements in Generative Adversarial Networks (GANs) in computer vision aswell as in medical imaging may provide a basis for enhanced capabilities incancer detection and analysis. In this review, we assess the potential of GANsto address a number of key challenges of cancer imaging, including datascarcity and imbalance, domain and dataset shifts, data access and privacy,data annotation and quantification, as well as cancer detection, tumourprofiling and treatment planning. We provide a critical appraisal of theexisting literature of GANs applied to cancer imagery, together withsuggestions on future research directions to address these challenges. Weanalyse and discuss 163 papers that apply adversarial training techniques inthe context of cancer imaging and elaborate their methodologies, advantages andlimitations. With this work, we strive to bridge the gap between the needs ofthe clinical cancer imaging community and the current and prospective researchon GANs in the artificial intelligence community.
AU  - Osuala,R
AU  - Kushibar,K
AU  - Garrucho,L
AU  - Linardos,A
AU  - Szafranowska,Z
AU  - Klein,S
AU  - Glocker,B
AU  - Diaz,O
AU  - Lekadir,K
PB  - arXiv
PY  - 2021///
TI  - A review of generative adversarial networks in cancer imaging: new applications, new solutions
UR  - http://arxiv.org/abs/2107.09543v1
UR  - http://hdl.handle.net/10044/1/90738
ER  -
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