Solving computer vision problems through machine learning, one often encounters lack of sufficient training data. To mitigate this we propose the use of ensembles of weak learners based on spectral total-variation (STV) features (Gilboa 2014). The features are related to nonlinear eigenfunctions of the total-variation subgradient and can characterize well textures at various scales. It was shown (Burger et-al 2016) that, in the one-dimensional case, orthogonal features are generated, whereas in two-dimensions the features are empirically lowly correlated. Ensemble learning theory advocates the use of lowly correlated weak learners. We thus propose here to design ensembles using learners based on STV features. To show the effectiveness of this paradigm we examine a hard real-world medical imaging problem: the predictive value of computed tomography (CT) data for high uptake in positron emission tomography (PET) for patients suspected of skeletal metastases. The database consists of 457 scans with 1524 unique pairs of registered CT and PET slices. Our approach is compared to deep-learning methods and to Radiomics features, showing STV learners perform best (AUC=0.87), compared to neural nets (AUC=0.75) and Radiomics (AUC=0.79). We observe that fine STV scales in CT images are especially indicative for the presence of high uptake in PET.

翻译：通过机器学习解决计算机视觉问题时，常面临训练数据不足的挑战。为缓解此问题，我们提出基于谱总变差（STV）特征（Gilboa 2014）的弱学习器集成方法。这些特征与总变差次梯度的非线性特征函数相关，能有效表征多尺度纹理。已有研究（Burger等人2016）表明，在一维情况下可生成正交特征，而在二维情况下特征经验证具有低相关性。集成学习理论倡导使用低相关性的弱学习器。因此，我们提出利用基于STV特征的学习器构建集成模型。为验证该范式的有效性，我们研究了一个具有挑战性的真实医学影像问题：计算机断层扫描（CT）数据对疑似骨转移患者正电子发射断层扫描（PET）高摄取区域的预测价值。数据库包含457次扫描的1524组配准CT与PET切片对。将本方法与深度学习方法及放射组学特征进行比较，结果显示STV学习器性能最优（AUC=0.87），优于神经网络（AUC=0.75）和放射组学方法（AUC=0.79）。我们发现CT图像中的精细STV尺度特征对PET高摄取区域的存在具有特别显著的指示作用。