Dynamic voting in multi-view learning for radiomics applications

Hongliu Cao; Simon Bernard; Laurent Heutte; Robert Sabourin

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arXiv · 1806.07686

Dynamic voting in multi-view learning for radiomics applications

Hongliu Cao·Simon Bernard·Laurent Heutte·Robert Sabourin

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Abstract

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.

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Hongliu Cao, Simon Bernard, Laurent Heutte, Robert Sabourin. 2018-06-20. Dynamic voting in multi-view learning for radiomics applications. https://arxiv.org/abs/1806.07686

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