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Journal of Imaging Informatics in Medicine

Erschienen in:

06.07.2018

A Decision-Support Tool for Renal Mass Classification

verfasst von: Gautam Kunapuli, Bino A. Varghese, Priya Ganapathy, Bhushan Desai, Steven Cen, Manju Aron, Inderbir Gill, Vinay Duddalwar

Erschienen in: Journal of Imaging Informatics in Medicine | Ausgabe 6/2018

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Abstract

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.

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Titel: A Decision-Support Tool for Renal Mass Classification
verfasst von: Gautam Kunapuli
Bino A. Varghese
Priya Ganapathy
Bhushan Desai
Steven Cen
Manju Aron
Inderbir Gill
Vinay Duddalwar
Publikationsdatum: 06.07.2018
Verlag: Springer International Publishing
Erschienen in: Journal of Imaging Informatics in Medicine / Ausgabe 6/2018
Print ISSN: 2948-2925
Elektronische ISSN: 2948-2933
DOI: https://doi.org/10.1007/s10278-018-0100-0

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A Decision-Support Tool for Renal Mass Classification

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