Imperial College London
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DR BERNHARD KAINZ

Faculty of Engineering, Department of Computing

Reader in Medical Image Computing
 
 
 
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Contact

 

+44 (0)20 7594 8349b.kainz Website CV

 
 
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Location

 

372Huxley BuildingSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@article{Rueckert:2016:10.1016/j.media.2016.06.009,
author = {Rueckert, D and Glocker, B and Kainz, B},
doi = {10.1016/j.media.2016.06.009},
journal = {Medical Image Analysis},
pages = {13--18},
title = {Learning clinically useful information from images: Past, present and future},
url = {http://dx.doi.org/10.1016/j.media.2016.06.009},
volume = {33},
year = {2016}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - Over the last decade, research in medical imaging has made significantprogress in addressing challenging tasks such as image registration and imagesegmentation. In particular, the use of model-based approaches has been keyin numerous, successful advances in methodology. The advantage of modelbasedapproaches is that they allow the incorporation of prior knowledgeacting as a regularisation that favours plausible solutions over implausibleones. More recently, medical imaging has moved away from hand-crafted, andoften explicitly designed models towards data-driven, implicit models thatare constructed using machine learning techniques. This has led to majorimprovements in all stages of the medical imaging pipeline, from acquisitionand reconstruction to analysis and interpretation. As more and more imagingdata is becoming available, e.g., from large population studies, this trend islikely to continue and accelerate. At the same time new developments inmachine learning, e.g., deep learning, as well as significant improvementsin computing power, e.g., parallelisation on graphics hardware, offer newpotential for data-driven, semantic and intelligent medical imaging. Thisarticle outlines the work of the BioMedIA group in this area and highlightssome of the challenges and opportunities for future work.
AU  - Rueckert,D
AU  - Glocker,B
AU  - Kainz,B
DO  - 10.1016/j.media.2016.06.009
EP  - 18
PY  - 2016///
SN  - 1361-8423
SP  - 13
TI  - Learning clinically useful information from images: Past, present and future
T2  - Medical Image Analysis
UR  - http://dx.doi.org/10.1016/j.media.2016.06.009
UR  - http://hdl.handle.net/10044/1/33719
VL  - 33
ER  -
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