Proposing the deep dynamic Bayesian network as a future computer based medical system
Data(s) |
16/08/2016
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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://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 |