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Deep learning application engine (DLAE): Development and integration of deep learning algorithms in medical imaging

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....

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Autores principales: Sanders, Jeremiah W., Fletcher, Justin R., Frank, Steven J., Liu, Ho-Ling, Johnson, Jason M., Zhou, Zijian, Chen, Henry Szu-Meng, Venkatesan, Aradhana M., Kudchadker, Rajat J., Pagel, Mark D., Ma, Jingfei
Formato: Online Artículo Texto
Lenguaje:English
Publicado: 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8188855/
https://www.ncbi.nlm.nih.gov/pubmed/34113706
http://dx.doi.org/10.1016/j.softx.2019.100347
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author Sanders, Jeremiah W.
Fletcher, Justin R.
Frank, Steven J.
Liu, Ho-Ling
Johnson, Jason M.
Zhou, Zijian
Chen, Henry Szu-Meng
Venkatesan, Aradhana M.
Kudchadker, Rajat J.
Pagel, Mark D.
Ma, Jingfei
author_facet Sanders, Jeremiah W.
Fletcher, Justin R.
Frank, Steven J.
Liu, Ho-Ling
Johnson, Jason M.
Zhou, Zijian
Chen, Henry Szu-Meng
Venkatesan, Aradhana M.
Kudchadker, Rajat J.
Pagel, Mark D.
Ma, Jingfei
author_sort Sanders, Jeremiah W.
collection PubMed
description 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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spelling pubmed-81888552021-06-09 Deep learning application engine (DLAE): Development and integration of deep learning algorithms in medical imaging Sanders, Jeremiah W. Fletcher, Justin R. Frank, Steven J. Liu, Ho-Ling Johnson, Jason M. Zhou, Zijian Chen, Henry Szu-Meng Venkatesan, Aradhana M. Kudchadker, Rajat J. Pagel, Mark D. Ma, Jingfei SoftwareX Article 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. 2019-10-29 2019 /pmc/articles/PMC8188855/ /pubmed/34113706 http://dx.doi.org/10.1016/j.softx.2019.100347 Text en https://creativecommons.org/licenses/by/4.0/This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) ).
spellingShingle Article
Sanders, Jeremiah W.
Fletcher, Justin R.
Frank, Steven J.
Liu, Ho-Ling
Johnson, Jason M.
Zhou, Zijian
Chen, Henry Szu-Meng
Venkatesan, Aradhana M.
Kudchadker, Rajat J.
Pagel, Mark D.
Ma, Jingfei
Deep learning application engine (DLAE): Development and integration of deep learning algorithms in medical imaging
title Deep learning application engine (DLAE): Development and integration of deep learning algorithms in medical imaging
title_full Deep learning application engine (DLAE): Development and integration of deep learning algorithms in medical imaging
title_fullStr Deep learning application engine (DLAE): Development and integration of deep learning algorithms in medical imaging
title_full_unstemmed Deep learning application engine (DLAE): Development and integration of deep learning algorithms in medical imaging
title_short Deep learning application engine (DLAE): Development and integration of deep learning algorithms in medical imaging
title_sort deep learning application engine (dlae): development and integration of deep learning algorithms in medical imaging
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8188855/
https://www.ncbi.nlm.nih.gov/pubmed/34113706
http://dx.doi.org/10.1016/j.softx.2019.100347
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