Big data analytics in medical imaging using deep learning Conference Paper


Authors: Tahmassebi, A.; Ehtemami, A.; Mohebali, B.; Gandomi, A. H.; Pinker, K.; Meyer-Baese, A.
Editor: Ahmad, F.
Title: Big data analytics in medical imaging using deep learning
Conference Title: Big Data: Learning, Analytics, and Applications
Abstract: Big data has been one of the hottest topics of scientific discussions in the recent years. In early 2000s, an industry analyst attempted to describe big data as the three Vs: Volume, Velocity, and Variability. With the new technologies such as Hadoop, it is now feasible to store and use extremely large volumes of data that comes in at an unprecedented velocity. The variability of this data can be large as it can come in different formats such as text documents, voice or video, and financial transactions. Big data analytics has been proven to be useful is various fields such as science, sports, advertising, health care, genomic sequence data, and medical imaging. This study presents a brief overview of big data analytics in medical imaging approaches with considering the importance of contemporary machine learning techniques such as deep learning. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.
Keywords: medical imaging; image processing; optimization; machine learning techniques; big data; deep learning; advanced analytics; data analytics; financial transactions; genomic-sequence data; industry analysts; large volumes; text document
Journal Title Proceedings of SPIE
Volume: 10989
Conference Dates: 2019 Apr 17-18
Conference Location: Baltimore, MD
ISBN: 0277-786X
Publisher: SPIE  
Date Published: 2019-01-01
Start Page: 109890E
Language: English
DOI: 10.1117/12.2516014
PROVIDER: scopus
DOI/URL:
Notes: Conference Paper -- Export Date: 1 October 2019 -- Source: Scopus
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