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Big data analytics in medical imaging using deep learning
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Paper 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.

Paper Details

Date Published: 13 May 2019
PDF: 16 pages
Proc. SPIE 10989, Big Data: Learning, Analytics, and Applications, 109890E (13 May 2019); doi: 10.1117/12.2516014
Show Author Affiliations
Amirhessam Tahmassebi, Florida State Univ. (United States)
Anahid Ehtemami, Florida State Univ. (United States)
Behshad Mohebali, Florida State Univ. (United States)
Amir H. Gandomi, Stevens Institute of Technology (United States)
Katja Pinker, Florida State Univ. (United States)
Memorial Sloan Kettering Cancer Ctr. (United States)
Medical Univ. of Vienna (Austria)
Anke Meyer-Baese, Florida State Univ. (United States)


Published in SPIE Proceedings Vol. 10989:
Big Data: Learning, Analytics, and Applications
Fauzia Ahmad, Editor(s)

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