Intelligent Imaging: Developing a Machine Learning Project.
Artificial intelligence (AI) has rapidly progressed, with exciting opportunities that drive enthusiasm for significant projects. A sensible and sustainable approach would be to start building an AI footprint with smaller, machine learning (ML)-based initiatives using artificial neural networks befor...
| Publicado en: | Journal of Nuclear Medicine Technology Vol. 48; no. 4; pp. 1 - 16 |
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| Autores principales: | , |
| Formato: | Journal Article |
| Publicado: |
Society of Nuclear Medicine
Dec2020
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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=147786401&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 147786401 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 00914916 H7Q jtl: Journal of Nuclear Medicine Technology issn: 00914916 maglogo: N pubinfo: dt: Dec2020 vid: 48 iid: 4 pid: 2576 pub: Society of Nuclear Medicine place: Reston, Virginia artinfo: ui: 147786401 147786401 NLM33361185 10.2967/jnmt.120.256628 NLM33361185 147786401 ppf: 1 ppct: 15 formats: fmt: @attributes: type: P tig: atl: Intelligent Imaging: Developing a Machine Learning Project. aug: au: Currie, Geoff Currie, Geoffrey M affil: School of Dentistry & Health Sciences, Charles Sturt University, Wagga Wagga, Australia sug: subj: Artificial Intelligence Impact of Events Scale ab: Artificial intelligence (AI) has rapidly progressed, with exciting opportunities that drive enthusiasm for significant projects. A sensible and sustainable approach would be to start building an AI footprint with smaller, machine learning (ML)-based initiatives using artificial neural networks before progressing to more complex deep learning (DL) approaches using convolutional neural networks. Several strategies and examples of entry-level projects are outlined, including mock potential projects using convolutional neural networks toward which we can progress. The examples provide a narrow snapshot of potential applications designed to inspire readers to think outside the box at problem solving using AI and ML. The simple and resource-light ML approaches are ideal for problem solving, are accessible starting points for developing an institutional AI program, and provide solutions that can have a significant and immediate impact on practice. A logical approach would be to use ML to examine the problem and identify among the broader ML projects which problems are most likely to benefit from a DL approach. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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