Collaborative and Reproducible Research: Goals, Challenges, and Strategies.
J Digit Imaging
; 31(3): 275-282, 2018 06.
Article
em En
| MEDLINE
| ID: mdl-29476392
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.
Palavras-chave
Texto completo:
1
Base de dados:
MEDLINE
Assunto principal:
Pesquisa
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Processamento de Imagem Assistida por Computador
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Diagnóstico por Imagem
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Aprendizado de Máquina
Tipo de estudo:
Diagnostic_studies
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Guideline
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Prognostic_studies
Limite:
Humans
Idioma:
En
Ano de publicação:
2018
Tipo de documento:
Article