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

Jeremiah W. Sanders, Justin R. Fletcher, Steven J. Frank, Ho Ling Liu, Jason M. Johnson, Zijian Zhou, Henry Szu Meng Chen, Aradhana M. Venkatesan, Rajat J. Kudchadker, Mark D. Pagel, Jingfei Ma

Research output: Contribution to journalArticlepeer-review

3 Scopus citations

Abstract

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.

Original languageEnglish (US)
Article number100347
JournalSoftwareX
Volume10
DOIs
StatePublished - Jul 1 2019

Keywords

  • Algorithm development
  • Deep learning
  • Medical imaging
  • Software

ASJC Scopus subject areas

  • Software
  • Computer Science Applications

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