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Deep learning application engine (DLAE): Development and integration of deep learning algorithms in medical imaging
Author(s) -
Jeremiah Sanders,
Justin Fletcher,
Steven J. Frank,
HoLing Liu,
Jason M. Johnson,
Zijian Zhou,
Henry Chen,
Aradhana M. Venkatesan,
Rajat J. Kudchadker,
Mark D. Pagel,
Jingfei Ma
Publication year - 2019
Publication title -
softwarex
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.528
H-Index - 21
ISSN - 2352-7110
DOI - 10.1016/j.softx.2019.100347
Subject(s) - computer science , deep learning , convolutional neural network , workflow , artificial intelligence , software deployment , software , machine learning , medical imaging , source code , algorithm , software engineering , programming language , database
Herein we introduce a deep learning (DL) application engine (DLAE) system concept, present potential uses of it, and describe pathways for its integration in clinical workflows. An open-source software application was developed to provide a code-free approach to DL for medical imaging applications. DLAE supports several DL techniques used in medical imaging, including convolutional neural networks, fully convolutional networks, generative adversarial networks, and bounding box detectors. Several example applications using clinical images were developed and tested to demonstrate the capabilities of DLAE. Additionally, a model deployment example was demonstrated in which DLAE was used to integrate two trained models into a commercial clinical software package.

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