Harmonization of methods to facilitate reproducibility in medical data processing: Applications to diffusion tensor magnetic resonance imaging

Jeffrey Jenkins, Lin Ching Chang, Elizabeth Hutchinson, M. Okan Irfanoglu, Carlo Pierpaoli

Research output: Chapter in Book/Report/Conference proceedingConference contribution

6 Scopus citations

Abstract

Data and methodology sharing is essential for progression of scientific research. Several research groups have built tools for medical big data (MBD) processing applicable to Diffusion Tensor MRI (DTI) processing pipelines. In this paper, we propose a framework enabling methodology sharing (i.e. harmonization) to facilitate the reproducibility in DTI processing.

Original languageEnglish (US)
Title of host publicationProceedings - 2016 IEEE International Conference on Big Data, Big Data 2016
EditorsRonay Ak, George Karypis, Yinglong Xia, Xiaohua Tony Hu, Philip S. Yu, James Joshi, Lyle Ungar, Ling Liu, Aki-Hiro Sato, Toyotaro Suzumura, Sudarsan Rachuri, Rama Govindaraju, Weijia Xu
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages3992-3994
Number of pages3
ISBN (Electronic)9781467390040
DOIs
StatePublished - 2016
Externally publishedYes
Event4th IEEE International Conference on Big Data, Big Data 2016 - Washington, United States
Duration: Dec 5 2016Dec 8 2016

Publication series

NameProceedings - 2016 IEEE International Conference on Big Data, Big Data 2016

Other

Other4th IEEE International Conference on Big Data, Big Data 2016
Country/TerritoryUnited States
CityWashington
Period12/5/1612/8/16

Keywords

  • diffusion tensor MRI
  • medical big data
  • method sharing
  • reproducibility
  • workflow automation

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Information Systems
  • Hardware and Architecture

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