Collaborative and Reproducible Research: Goals, Challenges, and Strategies

Steve G. Langer, George Shih, Paul G Nagy, Bennet A. Landman

Research output: Contribution to journalArticle

Abstract

Combining imaging biomarkers with genomic and clinical phenotype data is the foundation of precision medicine research efforts. Yet, biomedical imaging research requires unique infrastructure compared with principally text-driven clinical electronic medical record (EMR) data. The issues are related to the binary nature of the file format and transport mechanism for medical images as well as the post-processing image segmentation and registration needed to combine anatomical and physiological imaging data sources. The SiiM Machine Learning Committee was formed to analyze the gaps and challenges surrounding research into machine learning in medical imaging and to find ways to mitigate these issues. At the 2017 annual meeting, a whiteboard session was held to rank the most pressing issues and develop strategies to meet them. The results, and further reflections, are summarized in this paper.

Original languageEnglish (US)
Pages (from-to)1-8
Number of pages8
JournalJournal of Digital Imaging
DOIs
Publication statusAccepted/In press - Feb 23 2018

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Keywords

  • Computer analytics
  • Computers in medicine
  • Machine learning

ASJC Scopus subject areas

  • Radiological and Ultrasound Technology
  • Radiology Nuclear Medicine and imaging
  • Computer Science Applications

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