A DICOM Framework for Machine Learning and Processing Pipelines Against Real-time Radiology Images

Real-time execution of machine learning (ML) pipelines on radiology images is difficult due to limited computing resources in clinical environments, whereas running them in research clusters requires efficient data transfer capabilities. We developed Niffler, an open-source Digital Imaging and Commu...

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Detalles Bibliográficos
Autores principales: Kathiravelu, Pradeeban, Sharma, Puneet, Sharma, Ashish, Banerjee, Imon, Trivedi, Hari, Purkayastha, Saptarshi, Sinha, Priyanshu, Cadrin-Chenevert, Alexandre, Safdar, Nabile, Gichoya, Judy Wawira
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer International Publishing 2021
Materias:
Original Paper
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8455728/
https://www.ncbi.nlm.nih.gov/pubmed/34405297
http://dx.doi.org/10.1007/s10278-021-00491-w

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8455728/
https://www.ncbi.nlm.nih.gov/pubmed/34405297
http://dx.doi.org/10.1007/s10278-021-00491-w

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