Imperial College London
Alternate

Professor Anil Anthony Bharath

Faculty of Engineering, Department of Bioengineering

Academic Director (Singapore)
 
 
 
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Contact

 

+44 (0)20 7594 5463a.bharath Website

 
 
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Location

 

4.12Royal School of MinesSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@inproceedings{Balaram:2019:10.1007/978-3-030-32692-0_46,
author = {Balaram, S and Arulkumaran, K and Dai, T and Bharath, AA},
doi = {10.1007/978-3-030-32692-0_46},
pages = {400--408},
publisher = {SPRINGER INTERNATIONAL PUBLISHING AG},
title = {A maximum entropy deep reinforcement learning neural tracker},
url = {http://dx.doi.org/10.1007/978-3-030-32692-0_46},
year = {2019}
}
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RIS format (EndNote, RefMan)

TY  - CPAPER
AB  - Tracking of anatomical structures has multiple applications in the field of biomedical imaging, including screening, diagnosing and monitoring the evolution of pathologies. Semi-automated tracking of elongated structures has been previously formulated as a problem suitable for deep reinforcement learning (DRL), but it remains a challenge. We introduce a maximum entropy continuous-action DRL neural tracker capable of training from scratch in a complex environment in the presence of high noise levels, Gaussian blurring and detractors. The trained model is evaluated on two-photon microscopy images of mouse cortex. At the expense of slightly worse robustness compared to a previously applied DRL tracker, we reach significantly higher accuracy, approaching the performance of the standard hand-engineered algorithm used for neuron tracing. The higher sample efficiency of our maximum entropy DRL tracker indicates its potential of being applied directly to small biomedical datasets.
AU  - Balaram,S
AU  - Arulkumaran,K
AU  - Dai,T
AU  - Bharath,AA
DO  - 10.1007/978-3-030-32692-0_46
EP  - 408
PB  - SPRINGER INTERNATIONAL PUBLISHING AG
PY  - 2019///
SN  - 0302-9743
SP  - 400
TI  - A maximum entropy deep reinforcement learning neural tracker
UR  - http://dx.doi.org/10.1007/978-3-030-32692-0_46
UR  - http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000548437800046&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=1ba7043ffcc86c417c072aa74d649202
UR  - https://link.springer.com/chapter/10.1007%2F978-3-030-32692-0_46
UR  - http://hdl.handle.net/10044/1/83260
ER  -
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