Collaborative and Reproducible Research: Goals, Challenges, and Strategies.
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...
| Publicado en: | Journal of Digital Imaging Vol. 31; no. 3; pp. 275 - 283 |
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| Autores principales: | , , , |
| Formato: | tables/charts Journal Article |
| Publicado: |
Springer Nature
Jun2018
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=129685402&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 129685402 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08971889 DOQ jtl: Journal of Digital Imaging issn: 08971889 maglogo: N pubinfo: dt: Jun2018 vid: 31 iid: 3 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 129685402 129685402 129685402 10.1007/s10278-017-0043-x 129685402 ppf: 275 ppct: 8 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Collaborative and Reproducible Research: Goals, Challenges, and Strategies. aug: au: Langer, Steve G. Shih, George Nagy, Paul Landman, Bennet A. affil: Radiology, Mayo Clinic, Rochester, MN, USA sug: subj: Collaboration Machine Learning Diagnostic Imaging Individualized Medicine Committees Biological Markers ab: 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. pubtype: Academic Journal doctype: tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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