UtopiaCompression Corporation, 11150 W Olympic Blvd. Suite #820, Los Angeles, CA, 90064, USA.
Department of Radiology, Keck School of Medicine, University of Southern California, 1500 San Pablo Street, 2nd Floor, Los Angeles, CA, 90033, USA.
J Digit Imaging. 2018 Dec;31(6):929-939. doi: 10.1007/s10278-018-0100-0.
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.
我们研究了统计关系机器学习算法在使用基于放射组学的成像特征识别肾肿块恶性程度任务中的可行性。从多期对比增强 CT 图像中提取了描述肾肿块纹理、信号强度和其他相关指标的特征。最近开发的关系功能梯度提升 (RFGB) 形式主义被用于学习人类可解释的分类模型。实验结果表明，RFGB 优于许多标准机器学习方法以及当前由放射科医生进行视觉定性的诊断金标准。
