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J Digit Imaging. 2018 Jun;31(3):275-282. doi: 10.1007/s10278-017-0043-x.

Collaborative and Reproducible Research: Goals, Challenges, and Strategies.

Journal of digital imaging

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


  1. Radiology, Mayo Clinic, Rochester, MN, USA. [email protected].
  2. Department of Radiology, Weill Cornell Medicine, New York, NY, USA.
  3. Russell H. Morgan Department of Radiology and Radiological Sciences, Johns Hopkins University, Baltimore, MD, USA.
  4. Electrical Engineering, Vanderbilt University, Nashville, TN, 37235, USA.

PMID: 29476392 PMCID: PMC5959829 DOI: 10.1007/s10278-017-0043-x


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.

Keywords: Computer analytics; Computers in medicine; Machine learning


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