Toolkits and Libraries for Deep Learning

Bradley J. Erickson, Panagiotis Korfiatis, Zeynettin Akkus, Timothy Kline, Kenneth Philbrick

Research output: Contribution to journalReview article

44 Scopus citations

Abstract

Deep learning is an important new area of machine learning which encompasses a wide range of neural network architectures designed to complete various tasks. In the medical imaging domain, example tasks include organ segmentation, lesion detection, and tumor classification. The most popular network architecture for deep learning for images is the convolutional neural network (CNN). Whereas traditional machine learning requires determination and calculation of features from which the algorithm learns, deep learning approaches learn the important features as well as the proper weighting of those features to make predictions for new data. In this paper, we will describe some of the libraries and tools that are available to aid in the construction and efficient execution of deep learning as applied to medical images.

Original languageEnglish (US)
Pages (from-to)400-405
Number of pages6
JournalJournal of Digital Imaging
Volume30
Issue number4
DOIs
StatePublished - Aug 1 2017

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Keywords

  • Artificial intelligence
  • Convolutional neural network
  • Deep learning
  • Machine learning

ASJC Scopus subject areas

  • Radiological and Ultrasound Technology
  • Radiology Nuclear Medicine and imaging
  • Computer Science Applications

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