Collaborative and Reproducible Research: Goals, Challenges, and Strategies.
J Digit Imaging
; 31(3): 275-282, 2018 06.
Article
en 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.
Palabras clave
Texto completo:
1
Colección:
01-internacional
Banco de datos:
MEDLINE
Asunto principal:
Investigación
/
Procesamiento de Imagen Asistido por Computador
/
Diagnóstico por Imagen
/
Aprendizaje Automático
Tipo de estudio:
Diagnostic_studies
/
Guideline
/
Prognostic_studies
Límite:
Humans
Idioma:
En
Revista:
J Digit Imaging
Asunto de la revista:
DIAGNOSTICO POR IMAGEM
/
INFORMATICA MEDICA
/
RADIOLOGIA
Año:
2018
Tipo del documento:
Article
País de afiliación:
Estados Unidos