Journal
JOURNAL OF DIGITAL IMAGING
Volume 30, Issue 4, Pages 400-405Publisher
SPRINGER
DOI: 10.1007/s10278-017-9965-6
Keywords
Artificial intelligence; Machine learning; Deep learning; Convolutional neural network
Funding
- National Cancer Institute [U01 CA160045]
- NIDDK [P30 DK090728]
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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.
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