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Review
. 2017 Aug;30(4):400-405.
doi: 10.1007/s10278-017-9965-6.

Toolkits and Libraries for Deep Learning

Bradley J Erickson  1 , Panagiotis Korfiatis  2 , Zeynettin Akkus  2 , Timothy Kline  2 , Kenneth Philbrick  2
Affiliations
Review

Toolkits and Libraries for Deep Learning

Bradley J Erickson et al. J Digit Imaging. 2017 Aug.

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.

Keywords: Artificial intelligence; Convolutional neural network; Deep learning; Machine learning.

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Figures

Fig. 1
Fig. 1
Example code implementing LeNet CNN written in Caffe
Fig. 2
Fig. 2
Example code implementing LeNet CNN written in Keras
Fig. 3
Fig. 3
Example of PyTorch code and block diagram equivalent
See this image and copyright information in PMC

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