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首页> 外文会议>IEEE International Conference on Systems, Man, and Cybernetics >Correlation Based Random Subspace Ensembles for Predicting Number of Axillary Lymph Node Metastases in Breast DCE-MRI Tumors
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Correlation Based Random Subspace Ensembles for Predicting Number of Axillary Lymph Node Metastases in Breast DCE-MRI Tumors

机译：基于相关性的随机子空间集合预测乳腺癌DCE-MRI肿瘤腋窝淋巴结转移的数量

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摘要

An important problem in quantitative medical image analysis is a large number of features (often highly correlated) to instance ratio. To handle this, we developed a feature selector and an ensemble classifier based on a modified version of random subspace method. We propose using a fusion of feature selection concepts: ranking based, correlation based and random subspaces, to develop a concordance correlation coefficient based random subspace method (CCC RSM) feature selector. It forms random feature subsets with weakly correlated yet relevant features while the ensemble classification is achieved by training the base classifier with these feature subsets. Axillary lymph node (ALNs) metastases is one of the most important prognostic factors in breast cancer. We applied CCC RSM for four binary class classifications based on the number of metastatic ALNs: (i) = 1 vs 0 (ii) 1-3 vs 0 (iii) 1-3 vs = 4, and (iv) 4 vs 0. We extracted textural kinetics from habitats of fifty eight dynamic contrast enhanced magnetic resonance imaging breast tumors. We used three classifiers to compare the accuracies achieved by CCC RSM with random subspaces (RS), wrappers and correlation based feature selector (CFS). For each binary classification we achieved the best accuracy (= 78%) using CCC RSM.
机译：定量医学图像分析中的一个重要问题是大量特征（通常高度相关）与实例比率。为了解决这个问题，我们基于随机子空间方法的修改版开发了特征选择器和集成分类器。我们建议使用特征选择概念的融合：基于排名，基于相关和随机子空间，以开发基于一致性相关系数的随机子空间方法（CCC RSM）特征选择器。它形成具有弱相关但相关特征的随机特征子集，而集成分类是通过用这些特征子集训练基本分类器来实现的。腋窝淋巴结转移是乳腺癌最重要的预后因素之一。我们根据转移性ALN的数量将CCC RSM应用于四个二进制类别分类：（i）= 1 vs 0（ii）1-3 vs 0（iii）1-3 vs = 4，（iv）4 vs 0。我们从58个动态对比度增强的磁共振成像乳腺肿瘤的栖息地中提取了质构动力学。我们使用三个分类器来比较CCC RSM与随机子空间（RS），包装器和基于相关的特征选择器（CFS）所获得的精度。对于每个二进制分类，我们使用CCC RSM达到了最高的准确性（= 78％）。

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