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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Publicado en:	Journal of Digital Imaging Vol. 34; no. 4; pp. 1005 - 1014
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:	diagnostic images tables/charts Journal Article
Publicado:	Springer Nature Aug2021
Acceso en línea:	Ver este registro en EBSCOhost