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...
| Publicado en: | Journal of Digital Imaging Vol. 30; no. 4; pp. 400 - 406 |
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| Autores principales: | , , , , |
| Formato: | computer program tables/charts Journal Article |
| Publicado: |
Springer Nature
Aug2017
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=124395651&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 124395651 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08971889 DOQ jtl: Journal of Digital Imaging issn: 08971889 maglogo: N pubinfo: dt: Aug2017 vid: 30 iid: 4 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 124395651 124395651 143937125 124395651 10.1007/s10278-017-9965-6 124395651 ppf: 400 ppct: 6 formats: fmt: @attributes: type: P tig: atl: Toolkits and Libraries for Deep Learning. aug: au: Erickson, Bradley Korfiatis, Panagiotis Akkus, Zeynettin Kline, Timothy Philbrick, Kenneth affil: Mayo Clinic , 200 First St SW Rochester 55905 USA sug: subj: Artificial Intelligence Neural Networks (Computer) Diagnostic Imaging Algorithms Software ab: 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. pubtype: Academic Journal doctype: computer program tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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