论文标题
统计探索常规和不可知论特征与可解释风险表征的关系
Statistical Exploration of Relationships Between Routine and Agnostic Features Towards Interpretable Risk Characterization
论文作者
论文摘要
正如在高通量系统应用的其他领域中的典型情况一样，放射学面临着解释日益复杂的预测模型的挑战，例如从放射线分析中得出的预测模型。解释可以通过机器学习模型的学习输出来指导，但是每种技术可能会有很大的变化。无论该输出模型如何，都会提出一些基本问题。我们如何解释用于临床实施的预后模型？我们如何在放射线特征集中确定潜在的信息结构，以创建临床上可解释的模型？以及我们如何重组或利用特征之间的潜在关系降至改善的可解释性？探索了许多统计技术，以评估放射学特征的（可能是非线性）的关系。
As is typical in other fields of application of high throughput systems, radiology is faced with the challenge of interpreting increasingly sophisticated predictive models such as those derived from radiomics analyses. Interpretation may be guided by the learning output from machine learning models, which may however vary greatly with each technique. Whatever this output model, it will raise some essential questions. How do we interpret the prognostic model for clinical implementation? How can we identify potential information structures within sets of radiomic features, in order to create clinically interpretable models? And how can we recombine or exploit potential relationships between features towards improved interpretability? A number of statistical techniques are explored to assess (possibly nonlinear) relationships between radiological features from different angles.
