# Dynamic voting in multi-view learning for radiomics applications

arXiv: https://arxiv.org/abs/1806.07686 · HTML: https://mlsanity.com/paper/1806.07686/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Jun 2018.

## Built on by (most cited first, 4 of 4)

- [Random Forest Kernel for High-Dimension Low Sample Size Classification](https://mlsanity.com/paper/2310.14710/) (2023)
- [A Multiple Classifier Approach for Concatenate-Designed Neural Networks](https://mlsanity.com/paper/2101.05457/) (2021)
- [Multi-view user representation learning for user matching without personal information](https://mlsanity.com/paper/2312.14533/) (2023)
- [Holistic analysis on the sustainability of Federated Learning across AI product lifecycle](https://mlsanity.com/paper/2312.14628/) (2023)

## Abstract (arXiv)

Cancer diagnosis and treatment often require a personalized analysis for each patient nowadays, due to the heterogeneity among the different types of tumor and among patients. Radiomics is a recent medical imaging field that has shown during the past few years to be promising for achieving this personalization. However, a recent study shows that most of the state-of-the-art works in Radiomics fail to identify this problem as a multi-view learning task and that multi-view learning techniques are generally more efficient. In this work, we propose to further investigate the potential of one family of multi-view learning methods based on Multiple Classifiers Systems where one classifier is learnt on each view and all classifiers are combined afterwards. In particular, we propose a random forest based dynamic weighted voting scheme, which personalizes the combination of views for each new patient for classification tasks. The proposed method is validated on several real-world Radiomics problems.