Toolkits and Libraries for Deep Learning.
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.
Mayo Clinic, 200 First St SW, Rochester, MN, 55905, USA. bje@Mayo.edu.
Bradley J
BJ
Erickson
Mayo Clinic, 200 First St SW, Rochester, MN, 55905, USA.
Panagiotis
P
Korfiatis
Mayo Clinic, 200 First St SW, Rochester, MN, 55905, USA.
Zeynettin
Z
Akkus
Mayo Clinic, 200 First St SW, Rochester, MN, 55905, USA.
Timothy
T
Kline
Mayo Clinic, 200 First St SW, Rochester, MN, 55905, USA.
Kenneth
K
Philbrick
D000465:Algorithms; D003952:Diagnostic Imaging; D004282:Documentation; D006801:Humans; D000069550:Machine Learning; D016571:Neural Networks, Computer; D012984:Software
Artificial intelligence; Convolutional neural network; Deep learning; Machine learning