Proposing the deep dynamic Bayesian network as a future computer based medical system

Caoimhe M. Carbery; Adele H. Marshall; Roger Woods

doi:10.1109/CBMS.2016.70

Proposing the deep dynamic Bayesian network as a future computer based medical system

Caoimhe M. Carbery, Adele H. Marshall, Roger Woods

Queen's University Belfast

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

1 Citation (Scopus)

482 Downloads (Pure)

Abstract

The development of new learning models has been of great importance throughout recent years, with a focus on creating advances in the area of deep learning. Deep learning was first noted in 2006, and has since become a major area of research in a number of disciplines. This paper will delve into the area of deep learning to present its current limitations and provide a new idea for a fully integrated deep and dynamic probabilistic system. The new model will be applicable to a vast number of areas initially focusing on applications into medical image analysis with an overall goal of utilising this approach for prediction purposes in computer based medical systems.

Original language	English
Title of host publication	Proceedings - IEEE 29th International Symposium on Computer-Based Medical Systems, CBMS 2016
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	227-228
Number of pages	2
ISBN (Electronic)	9781467390361
DOIs	https://doi.org/10.1109/CBMS.2016.70
Publication status	Published - 18 Aug 2016
Event	29th IEEE International Symposium on Computer-Based Medical Systems, CBMS 2016 - Belfast, United Kingdom Duration: 20 Jun 2016 → 23 Jun 2016

Publication series

Name	IEEE 29th International Symposium on Computer-Based Medical Systems (CBMS)
Publisher	Institute of Electrical and Electronics Engineers
ISSN (Print)	2372=9198

Conference

Conference	29th IEEE International Symposium on Computer-Based Medical Systems, CBMS 2016
Country	United Kingdom
City	Belfast
Period	20/06/2016 → 23/06/2016

Keywords

Deep learning
Dynamic Bayesian network
Medical systems
Probabilistic graphical model

ASJC Scopus subject areas

Radiology Nuclear Medicine and imaging
Computer Science Applications

Access to Document

10.1109/CBMS.2016.70Licence: Unspecified

Proposing the deep dynamic Bayesian network
© 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Accepted author manuscript, 116 KBLicence: Unspecified

Link to publication in Scopus

Fingerprint Dive into the research topics of 'Proposing the deep dynamic Bayesian network as a future computer based medical system'. Together they form a unique fingerprint.

Cite this

Carbery, C. M., Marshall, A. H., & Woods, R. (2016). Proposing the deep dynamic Bayesian network as a future computer based medical system. In Proceedings - IEEE 29th International Symposium on Computer-Based Medical Systems, CBMS 2016 (pp. 227-228). [7545991] (IEEE 29th International Symposium on Computer-Based Medical Systems (CBMS)). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CBMS.2016.70

Carbery, Caoimhe M. ; Marshall, Adele H. ; Woods, Roger. / Proposing the deep dynamic Bayesian network as a future computer based medical system. Proceedings - IEEE 29th International Symposium on Computer-Based Medical Systems, CBMS 2016. Institute of Electrical and Electronics Engineers Inc., 2016. pp. 227-228 (IEEE 29th International Symposium on Computer-Based Medical Systems (CBMS)).

@inproceedings{29f6a066d28142efaede5546091633c6,

title = "Proposing the deep dynamic Bayesian network as a future computer based medical system",

abstract = "The development of new learning models has been of great importance throughout recent years, with a focus on creating advances in the area of deep learning. Deep learning was first noted in 2006, and has since become a major area of research in a number of disciplines. This paper will delve into the area of deep learning to present its current limitations and provide a new idea for a fully integrated deep and dynamic probabilistic system. The new model will be applicable to a vast number of areas initially focusing on applications into medical image analysis with an overall goal of utilising this approach for prediction purposes in computer based medical systems.",

keywords = "Deep learning, Dynamic Bayesian network, Medical systems, Probabilistic graphical model",

author = "Carbery, {Caoimhe M.} and Marshall, {Adele H.} and Roger Woods",

year = "2016",

month = aug,

day = "18",

doi = "10.1109/CBMS.2016.70",

language = "English",

series = "IEEE 29th International Symposium on Computer-Based Medical Systems (CBMS)",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "227--228",

booktitle = "Proceedings - IEEE 29th International Symposium on Computer-Based Medical Systems, CBMS 2016",

note = "29th IEEE International Symposium on Computer-Based Medical Systems, CBMS 2016 ; Conference date: 20-06-2016 Through 23-06-2016",

}

Carbery, CM, Marshall, AH & Woods, R 2016, Proposing the deep dynamic Bayesian network as a future computer based medical system. in Proceedings - IEEE 29th International Symposium on Computer-Based Medical Systems, CBMS 2016., 7545991, IEEE 29th International Symposium on Computer-Based Medical Systems (CBMS), Institute of Electrical and Electronics Engineers Inc., pp. 227-228, 29th IEEE International Symposium on Computer-Based Medical Systems, CBMS 2016, Belfast, United Kingdom, 20/06/2016. https://doi.org/10.1109/CBMS.2016.70

Proposing the deep dynamic Bayesian network as a future computer based medical system. / Carbery, Caoimhe M.; Marshall, Adele H.; Woods, Roger.

Proceedings - IEEE 29th International Symposium on Computer-Based Medical Systems, CBMS 2016. Institute of Electrical and Electronics Engineers Inc., 2016. p. 227-228 7545991 (IEEE 29th International Symposium on Computer-Based Medical Systems (CBMS)).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - Proposing the deep dynamic Bayesian network as a future computer based medical system

AU - Carbery, Caoimhe M.

AU - Marshall, Adele H.

AU - Woods, Roger

PY - 2016/8/18

Y1 - 2016/8/18

N2 - The development of new learning models has been of great importance throughout recent years, with a focus on creating advances in the area of deep learning. Deep learning was first noted in 2006, and has since become a major area of research in a number of disciplines. This paper will delve into the area of deep learning to present its current limitations and provide a new idea for a fully integrated deep and dynamic probabilistic system. The new model will be applicable to a vast number of areas initially focusing on applications into medical image analysis with an overall goal of utilising this approach for prediction purposes in computer based medical systems.

AB - The development of new learning models has been of great importance throughout recent years, with a focus on creating advances in the area of deep learning. Deep learning was first noted in 2006, and has since become a major area of research in a number of disciplines. This paper will delve into the area of deep learning to present its current limitations and provide a new idea for a fully integrated deep and dynamic probabilistic system. The new model will be applicable to a vast number of areas initially focusing on applications into medical image analysis with an overall goal of utilising this approach for prediction purposes in computer based medical systems.

KW - Deep learning

KW - Dynamic Bayesian network

KW - Medical systems

KW - Probabilistic graphical model

UR - http://www.scopus.com/inward/record.url?scp=84987606084&partnerID=8YFLogxK

U2 - 10.1109/CBMS.2016.70

DO - 10.1109/CBMS.2016.70

M3 - Conference contribution

AN - SCOPUS:84987606084

T3 - IEEE 29th International Symposium on Computer-Based Medical Systems (CBMS)

SP - 227

EP - 228

BT - Proceedings - IEEE 29th International Symposium on Computer-Based Medical Systems, CBMS 2016

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 29th IEEE International Symposium on Computer-Based Medical Systems, CBMS 2016

Y2 - 20 June 2016 through 23 June 2016

ER -

Carbery CM, Marshall AH , Woods R. Proposing the deep dynamic Bayesian network as a future computer based medical system. In Proceedings - IEEE 29th International Symposium on Computer-Based Medical Systems, CBMS 2016. Institute of Electrical and Electronics Engineers Inc. 2016. p. 227-228. 7545991. (IEEE 29th International Symposium on Computer-Based Medical Systems (CBMS)). https://doi.org/10.1109/CBMS.2016.70