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

arXiv: https://arxiv.org/abs/2507.08735 · HTML: https://mlsanity.com/paper/2507.08735/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 14 of its 45 references that are in the map.
- First posted on arXiv 11 Jul 2025.

## Builds on (most cited first, 14 of 14)

- [V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation](https://mlsanity.com/paper/1606.04797/) (2016)
- [Identity Mappings in Deep Residual Networks](https://mlsanity.com/paper/1603.05027/) (2016)
- [UNet++: A Nested U-Net Architecture for Medical Image Segmentation](https://mlsanity.com/paper/1807.10165/) (2018)
- [UNETR: Transformers for 3D Medical Image Segmentation](https://mlsanity.com/paper/2103.10504/) (2021)
- [CE-Net: Context Encoder Network for 2D Medical Image Segmentation](https://mlsanity.com/paper/1903.02740/) (2019)
- [ResUNet++: An Advanced Architecture for Medical Image Segmentation](https://mlsanity.com/paper/1911.07067/) (2019)
- [Cross-Modality Synthesis from CT to PET using FCN and GAN Networks for Improved Automated Lesion Detection](https://mlsanity.com/paper/1802.07846/) (2018)
- [Synthesis of Positron Emission Tomography (PET) Images via Multi-channel Generative Adversarial Networks (GANs)](https://mlsanity.com/paper/1707.09747/) (2017)
- [Spectral Decompositions using One-Homogeneous Functionals](https://mlsanity.com/paper/1601.02912/) (2016)
- [Nonlinear Spectral Geometry Processing via the TV Transform](https://mlsanity.com/paper/2009.03044/) (2020)
- [Multiscale Segmentation via Bregman Distances and Nonlinear Spectral Analysis](https://mlsanity.com/paper/1604.06665/) (2016)
- [Deeply Learned Spectral Total Variation Decomposition](https://mlsanity.com/paper/2006.10004/) (2020)
- [Unsupervised Learning of the Total Variation Flow](https://mlsanity.com/paper/2206.04406/) (2022)
- [DeepTV: A neural network approach for total variation minimization](https://mlsanity.com/paper/2409.05569/) (2024)

## Abstract (arXiv)

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.