Feature selection for tumor treatment outcome prediction in PET scans amounts to determine the best predictors in order to classify treatment outcomes for unseen data. Several challenges have to be addressed, notably the dissensus about the most predictive radiomic features and the relatively small-sized and imbalanced training samples. To overcome these issues, we propose a multi-label learning (MLL) problem formulation that we associated to the technique of feature selection with shared information among multiple tasks. In fact, we opt to simultaneously mining the shared knowledge across similar tasks from MRI and fused PET/MRI images in order to sort PET features according to their relevancy. Then, by repeatedly training an SVM model, different feature sets are elected to be informative for each iteration. Thus, by associating these sets with their correspondents in other iterations, the MLL formulation is performed in order to adequately adapt both feature sets and training instances according to their fit with unseen data. The adaptive synthetic sampling technique is adapted to the framework of MLL data balance. After that, the global and local label correlation technique is used for multi-labeling, while taking into consideration the capacity of MLL on recommending the best sets of features and training instances. Finally, an SVM classifier is applied with both selected features and training sub-samples in order to output prediction results. Conducted experiments show that attributing each feature set to a particular set of instances and training the model with balanced subset of the whole training data allows to outperform relevant methods from the state of the art.

中文翻译：

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基于共享信息和多标签学习的 PET 扫描自适应特征选择

PET 扫描中肿瘤治疗结果预测的特征选择相当于确定最佳预测因子，以便对未见数据的治疗结果进行分类。必须解决几个挑战，特别是关于最具预测性的放射组学特征和相对较小且不平衡的训练样本的分歧。为了克服这些问题，我们提出了一种多标签学习 (MLL) 问题公式，我们将其与在多个任务之间共享信息的特征选择技术相关联。事实上，我们选择同时从 MRI 和融合的 PET/MRI 图像中挖掘跨类似任务的共享知识，以便根据它们的相关性对 PET 特征进行排序。然后，通过反复训练 SVM 模型，选择不同的特征集为每次迭代提供信息。因此，通过在其他迭代中将这些集合与其对应方相关联，执行 MLL 公式，以便根据特征集和训练实例与看不见的数据的拟合情况，充分适应它们。自适应合成采样技术适用于MLL数据平衡的框架。之后，全局和局部标签相关技术用于多标签，同时考虑到 MLL 在推荐最佳特征集和训练实例方面的能力。最后，对选定的特征和训练子样本应用 SVM 分类器以输出预测结果。