A Decision-Support Tool for Renal Mass Classification.
We investigate the viability of statistical relational machine learning algorithms for the task of identifying malignancy of renal masses using radiomics-based imaging features. Features characterizing the texture, signal intensity, and other relevant metrics of the renal mass were extracted from mu...
| Publicado en: | Journal of Digital Imaging Vol. 31; no. 6; pp. 929 - 940 |
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| Autores principales: | , , , , , , , |
| Formato: | algorithm equations & formulas research tables/charts Journal Article |
| Publicado: |
Springer Nature
Dec2018
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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=133226233&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 133226233 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08971889 DOQ jtl: Journal of Digital Imaging issn: 08971889 maglogo: N pubinfo: dt: Dec2018 vid: 31 iid: 6 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 133226233 133226233 133226233 10.1007/s10278-018-0100-0 133226233 ppf: 929 ppct: 11 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: A Decision-Support Tool for Renal Mass Classification. aug: au: Kunapuli, Gautam Varghese, Bino A. Ganapathy, Priya Desai, Bhushan Cen, Steven Aron, Manju Gill, Inderbir Duddalwar, Vinay affil: UtopiaCompression Corporation, 11150 W Olympic Blvd. Suite #820, 90064, Los Angeles, CA, USA sug: subj: Decision Support Systems, Clinical Machine Learning Methods Kidney Neoplasms Classification Kidney Neoplasms Radiography Algorithms Tomography, X-Ray Computed Methods ab: We investigate the viability of statistical relational machine learning algorithms for the task of identifying malignancy of renal masses using radiomics-based imaging features. Features characterizing the texture, signal intensity, and other relevant metrics of the renal mass were extracted from multiphase contrast-enhanced computed tomography images. The recently developed formalism of relational functional gradient boosting (RFGB) was used to learn human-interpretable models for classification. Experimental results demonstrate that RFGB outperforms many standard machine learning approaches as well as the current diagnostic gold standard of visual qualification by radiologists. pubtype: Academic Journal doctype: algorithm equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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