Dynamic voting in multi-view learning for radiomics applications
This work addresses personalized cancer diagnosis for medical imaging applications, but it is incremental as it builds on existing multi-view learning methods.
The authors tackled the problem of personalized cancer diagnosis by proposing a dynamic weighted voting scheme for multi-view learning in radiomics, which personalizes the combination of views for each patient and was validated on real-world datasets.
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.