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M. Anthimopoulos, S. Christodoulidis, L. Ebner, A. Christe, S. Mougiakakou (2016)
Lung Pattern Classification for Interstitial Lung Diseases Using a Deep Convolutional Neural NetworkIEEE Transactions on Medical Imaging, 35
I. Goodfellow, David Warde-Farley, Pascal Lamblin, Vincent Dumoulin, Mehdi Mirza, Razvan Pascanu, J. Bergstra, Frédéric Bastien, Yoshua Bengio (2013)
Pylearn2: a machine learning research libraryArXiv, abs/1308.4214
Shijun Wang, R. Summers (2012)
Machine learning and radiologyMedical image analysis, 16 5
Geoffrey Hinton, Simon Osindero, Y. Teh (2006)
A Fast Learning Algorithm for Deep Belief NetsNeural Computation, 18
M. Grinsven, B. Ginneken, C. Hoyng, T. Theelen, C. Sánchez (2016)
Fast Convolutional Neural Network Training Using Selective Data Sampling: Application to Hemorrhage Detection in Color Fundus ImagesIEEE Transactions on Medical Imaging, 35
O. Ronneberger, P. Fischer, T. Brox (2015)
U-Net: Convolutional Networks for Biomedical Image SegmentationArXiv, abs/1505.04597
Yann LeCun, Yoshua Bengio (1998)
Convolutional networks for images, speech, and time series
Christian Szegedy, Sergey Ioffe, Vincent Vanhoucke, Alexander Alemi (2016)
Inception-v4, Inception-ResNet and the Impact of Residual Connections on LearningArXiv, abs/1602.07261
Nitish Srivastava, Geoffrey Hinton, A. Krizhevsky, Ilya Sutskever, R. Salakhutdinov (2014)
Dropout: a simple way to prevent neural networks from overfittingJ. Mach. Learn. Res., 15
A. Krizhevsky, Ilya Sutskever, Geoffrey Hinton (2012)
ImageNet classification with deep convolutional neural networksCommunications of the ACM, 60
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.
Journal of Digital Imaging – Springer Journals
Published: Mar 17, 2017
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