Toolkits and Libraries for Deep Learning

By Bradley James Erickson, P. Korfiatis, Zeynettin Akkus, T. Kline, Kenneth A. Philbrick. 2017

164 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

In Computer Vision and Pattern Recognition

Citations in the corpus

An overview of deep learning in medical imaging focusing on MRI (2018, background): “For example the comprehensive review [101],24 covering both medicine and biology and spanning from imaging applications in healthcare to protein-protein interaction and uncertainty quantification; key concepts of deep learning for clinical radiologists [102–111], including radiomics and imaging geno”
A Review on a Deep Learning Perspective in Brain Cancer Classification (2019, methodology): “There are some automatic brain tumor grading methods which were proposed by researchers based on texture analysis using ML techniques [92,104,105].”
Applications of Deep Learning and Reinforcement Learning to Biological Data (2017, methodology): “, see [75] for a mini review on tools based on DL), have been used in mining Biological data.”
Deep learning for smart fish farming: applications, opportunities and challenges (2020, background): “Whether used for specific commercial applications or experimental research, the combination of DL and transfer learning helps to reduce the need for a large amount of data while saving significant training time (Erickson et al., 2017).”
Hello World Deep Learning in Medical Imaging (2018, methodology): “There are many different libraries and machine learning frameworks available, including Caffe, MXNet, Tensorflow, Theano, Torch and PyTorch, which have facilitated machine learning research and application development [4].”