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• W2858745025 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." @default.
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• W2858745025 date "2018-07-06" @default.
• W2858745025 modified "2023-10-05" @default.
• W2858745025 title "A Decision-Support Tool for Renal Mass Classification" @default.
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• W2858745025 doi "https://doi.org/10.1007/s10278-018-0100-0" @default.
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