预处理器
再现性
直方图
计算机科学
人工智能
事实上
模式识别(心理学)
领域(数学)
特征(语言学)
体素
灵敏度(控制系统)
数据挖掘
图像(数学)
数学
统计
电子工程
语言学
工程类
哲学
政治学
法学
纯数学
作者
D. E. Wright,Cole J. Cook,Jason R. Klug,Panagiotis Korfiatis,Timothy L. Kline
出处
期刊:Cornell University - arXiv
日期:2022-01-01
标识
DOI:10.48550/arxiv.2211.05241
摘要
The de facto standard of dynamic histogram binning for radiomic feature extraction leads to an elevated sensitivity to fluctuations in annotated regions. This may impact the majority of radiomic studies published recently and contribute to issues regarding poor reproducibility of radiomic-based machine learning that has led to significant efforts for data harmonization; however, we believe the issues highlighted here are comparatively neglected, but often remedied by choosing static binning. The field of radiomics has improved through the development of community standards and open-source libraries such as PyRadiomics. But differences in image acquisition, systematic differences between observers' annotations, and preprocessing steps still pose challenges. These can change the distribution of voxels altering extracted features and can be exacerbated with dynamic binning.
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