  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.
