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


Autoria(s): Carbery, Caoimhe M.; Marshall, Adele H.; Woods, Roger
Data(s)

16/08/2016

Resumo

<p>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.</p>

Identificador

http://pure.qub.ac.uk/portal/en/publications/proposing-the-deep-dynamic-bayesian-network-as-a-future-computer-based-medical-system(29f6a066-d281-42ef-aede-5546091633c6).html

http://dx.doi.org/10.1109/CBMS.2016.70

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

Idioma(s)

eng

Publicador

Institute of Electrical and Electronics Engineers Inc.

Direitos

info:eu-repo/semantics/restrictedAccess

Fonte

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 . vol. 2016-August , 7545991 , Institute of Electrical and Electronics Engineers Inc. , pp. 227-228 , 29th IEEE International Symposium on Computer-Based Medical Systems, CBMS 2016 , Belfast , United Kingdom , 20-23 June . DOI: 10.1109/CBMS.2016.70

Palavras-Chave #Deep learning #Dynamic Bayesian network #Medical systems #Probabilistic graphical model #/dk/atira/pure/subjectarea/asjc/2700/2741 #Radiology Nuclear Medicine and imaging #/dk/atira/pure/subjectarea/asjc/1700/1706 #Computer Science Applications
Tipo

contributionToPeriodical