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Images in Space and Time: Real Big Data in Healthcare

Published: 13 July 2021 Publication History

Abstract

Medical imaging diagnosis is mostly subjective, as it depends on medical experts. Hence, the service provided is limited by expert opinion variations and image complexity as well. However, with the increasing advancements in deep learning field, techniques are developed to help in the diagnosis and risk assessment processes. In this article, we survey different types of images in healthcare. A review of the concept and research methodology of Radiomics will highlight the potentials of integrated diagnostics. Convolutional neural networks can play an important role in next generations of automated imaging biomarker extraction and big data analytics systems. Examples are provided of what is already feasible today and also describe additional technological components required for successful clinical implementation.

Supplementary Material

a113-badr-supp.pdf (badr.zip)
Supplemental movie, appendix, image and software files for, Images in Space and Time: Real Big Data in Healthcare

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cover image ACM Computing Surveys
ACM Computing Surveys  Volume 54, Issue 6
Invited Tutorial
July 2022
799 pages
ISSN:0360-0300
EISSN:1557-7341
DOI:10.1145/3475936
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Published: 13 July 2021
Accepted: 01 February 2021
Revised: 01 January 2021
Received: 01 August 2020
Published in CSUR Volume 54, Issue 6

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