A Decision-Support Tool for Renal Mass Classification.

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.

journal_name

J Digit Imaging

authors

Kunapuli G,Varghese BA,Ganapathy P,Desai B,Cen S,Aron M,Gill I,Duddalwar V

doi

10.1007/s10278-018-0100-0

subject

Has Abstract

pub_date

2018-12-01 00:00:00

pages

929-939

issue

6

eissn

0897-1889

issn

1618-727X

pii

10.1007/s10278-018-0100-0

journal_volume

31

pub_type

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