Ensemble of Weak Spectral Total Variation Learners: a PET-CT Case Study

TL;DR

This paper introduces an ensemble method of weak spectral total variation learners for medical imaging, demonstrating superior performance in predicting PET uptake from CT scans compared to deep learning and Radiomics.

Contribution

The paper presents a novel ensemble approach using spectral total variation features for medical image analysis, showing improved predictive accuracy over existing methods.

Findings

STV ensemble outperforms deep learning and Radiomics in AUC

Fine-scale STV features are highly indicative of PET uptake

Ensemble of weak learners benefits from low correlation among features

Abstract

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.