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A Multimodal Ensemble Driven by Multiobjective Optimisation to Predict Overall Survival in Non-Small-Cell Lung Cancer

Authors :
Camillo Maria Caruso
Valerio Guarrasi
Ermanno Cordelli
Rosa Sicilia
Silvia Gentile
Laura Messina
Michele Fiore
Claudia Piccolo
Bruno Beomonte Zobel
Giulio Iannello
Sara Ramella
Paolo Soda
Source :
Journal of Imaging, Vol 8, Iss 11, p 298 (2022)
Publication Year :
2022
Publisher :
MDPI AG, 2022.

Abstract

Lung cancer accounts for more deaths worldwide than any other cancer disease. In order to provide patients with the most effective treatment for these aggressive tumours, multimodal learning is emerging as a new and promising field of research that aims to extract complementary information from the data of different modalities for prognostic and predictive purposes. This knowledge could be used to optimise current treatments and maximise their effectiveness. To predict overall survival, in this work, we investigate the use of multimodal learning on the CLARO dataset, which includes CT images and clinical data collected from a cohort of non-small-cell lung cancer patients. Our method allows the identification of the optimal set of classifiers to be included in the ensemble in a late fusion approach. Specifically, after training unimodal models on each modality, it selects the best ensemble by solving a multiobjective optimisation problem that maximises both the recognition performance and the diversity of the predictions. In the ensemble, the labels of each sample are assigned using the majority voting rule. As further validation, we show that the proposed ensemble outperforms the models learning a single modality, obtaining state-of-the-art results on the task at hand.

Details

Language :
English
ISSN :
2313433X
Volume :
8
Issue :
11
Database :
Directory of Open Access Journals
Journal :
Journal of Imaging
Publication Type :
Academic Journal
Accession number :
edsdoj.9f43983c043040b2b41a7f20b0715321
Document Type :
article
Full Text :
https://doi.org/10.3390/jimaging8110298