Big Data Analytics in Medical Imaging using Deep Learning

被引：6

作者：

Tahmassebi, Amirhessam ^{[1
]}

Ehtemami, Anahid ^{[2
]}

Mohebali, Behshad ^{[1
]}

Gandomi, Amir H. ^{[3
]}

Pinker, Katja ^{[1
,4
,5
]}

Meyer-Base, Anke ^{[1
]}

机构：

[1] Florida State Univ, Dept Sci Comp, Tallahassee, FL 32306 USA

[2] FAMU FSU Coll Engn, Dept Elect & Comp Engn, Tallahassee, FL USA

[3] Stevens Inst Technol, Sch Business, Hoboken, NJ 07030 USA

[4] Mem Sloan Kettering Canc Ctr, Dept Radiol, Breast Imaging Serv, 1275 York Ave, New York, NY 10021 USA

[5] Med Univ Vienna, Div Mol & Gender Imaging, Dept Biomed Imaging & Image Guided Therapy, Vienna, Austria

来源：

BIG DATA: LEARNING, ANALYTICS, AND APPLICATIONS | 2019年 / 10989卷

关键词：

Big Data; Deep Learning; Medical Imaging; Image processing; Optimization; CT; REGION;

D O I：

10.1117/12.2516014

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

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.

引用

页数：16

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