Feature Importance in Nonlinear Embeddings (FINE): Applications in Digital Pathology
Feature Importance in Nonlinear Embeddings (FINE): Applications in Digital Pathology
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DOI：
10.1109/tmi.2015.2456188
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发表时间：
2016-01-01
影响因子：
10.6
通讯作者：
Madabhushi, Anant
中科院分区：
文献类型：
--
作者：
Ginsburg, Shoshana B.;Lee, George;Madabhushi, Anant
定量组织形态学（Quantitative histomorphometry， QH）是指通过提取数百个图像特征，在数字病理图像上对疾病外观进行计算建模，并利用它们预测疾病的存在或结果的过程。由于在高维特征空间中构建鲁棒且可解释的分类器具有挑战性，因此通常在构建分类器之前实现降维（DR）。然而，当进行DR时，量化每个原始特征对最终分类结果的贡献可能是一项挑战。我们之前提出了一种基于特征对通过主成分分析（PCA）衍生的嵌入的分类的重要性来对特征进行评分的方法。然而，非线性DR涉及核矩阵的特征分解，而不是数据本身，这使得分类器的可解释性问题更加复杂。在本文中，我们提出了非线性嵌入中的特征重要性（FINE），将基于PCA的特征评分方法扩展到核PCA (KPCA)，以及几种可以作为KPCA变体的NLDR算法。FINE应用于四个数字病理数据集，以确定预测乳腺癌和前列腺癌复发风险的关键QH特征。研究发现，核和腺体结构以及聚集性的测量在预测乳腺癌和前列腺癌复发的可能性方面起着重要作用。与t检验、Fisher分数和基尼指数相比，FINE能够识别出一组稳定的特征，这些特征在NIPS 2003特征选择挑战的四个公开可用数据集上提供了良好的分类准确性。
Quantitative histomorphometry (QH) refers to the process of computationally modeling disease appearance on digital pathology images by extracting hundreds of image features and using them to predict disease presence or outcome. Since constructing a robust and interpretable classifier is challenging in a high dimensional feature space, dimensionality reduction (DR) is often implemented prior to classifier construction. However, when DR is performed it can be challenging to quantify the contribution of each of the original features to the final classification result. We have previously presented a method for scoring features based on their importance for classification on an embedding derived via principal components analysis (PCA). However, nonlinear DR involves the eigen-decomposition of a kernel matrix rather than the data itself, compounding the issue of classifier interpretability. In this paper we present feature importance in nonlinear embeddings (FINE), an extension of our PCA-based feature scoring method to kernel PCA (KPCA), as well as several NLDR algorithms that can be cast as variants of KPCA. FINE is applied to four digital pathology datasets to identify key QH features for predicting the risk of breast and prostate cancer recurrence. Measures of nuclear and glandular architecture and clusteredness were found to play an important role in predicting the likelihood of recurrence of both breast and prostate cancers. Compared to the t-test, Fisher score, and Gini index, FINE was able to identify a stable set of features that provide good classification accuracy on four publicly available datasets from the NIPS 2003 Feature Selection Challenge.