Details

Autor(en) / Beteiligte

• 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

Titel

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

Ist Teil von

• SoftwareX, 2019-07, Vol.10, p.100347, Article 100347

Ort / Verlag

Elsevier B.V

Erscheinungsjahr

2019

Quelle

Free E-Journal (出版社公開部分のみ）

Beschreibungen/Notizen

• 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.