Big data analytics in medical imaging using deep learning

Amirhessam Tahmassebi; Anahid Ehtemami; Behshad Mohebali; Amir H. Gandomi; Katja Pinker; Anke Meyer-Baese

doi:10.1117/12.2516014

13 May 2019 Big data analytics in medical imaging using deep learning

Amirhessam Tahmassebi, Anahid Ehtemami, Behshad Mohebali, Amir H. Gandomi, Katja Pinker, Anke Meyer-Baese

Proceedings Volume 10989, Big Data: Learning, Analytics, and Applications; 109890E (2019) https://doi.org/10.1117/12.2516014
Event: SPIE Defense + Commercial Sensing, 2019, Baltimore, MD, United States

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.

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Citation Download Citation

Amirhessam Tahmassebi, Anahid Ehtemami, Behshad Mohebali, Amir H. Gandomi, Katja Pinker, and Anke Meyer-Baese "Big data analytics in medical imaging using deep learning", Proc. SPIE 10989, Big Data: Learning, Analytics, and Applications, 109890E (13 May 2019); https://doi.org/10.1117/12.2516014

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