Ensemble of Weak Spectral Total Variation Learners: a PET-CT Case Study
Journal:
arXiv
Published Date:
Jul 11, 2025
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.
