Toolkits and Libraries for Deep Learning

Journal of Digital Imaging
Bradley J EricksonKenneth Philbrick

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.

References

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Citations

May 18, 2018·IEEE Transactions on Neural Networks and Learning Systems·Mufti MahmudStefano Vassanelli
Oct 6, 2018·Future Medicinal Chemistry·Kristy A CarpenterXudong Huang
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Oct 1, 2020·Computational and Structural Biotechnology Journal·Erik Meijering
Aug 2, 2019··Baptiste AlcaldeBianca Schnalzer

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