Multimodal Neuroimaging Game Theoretic Data Fusion in Adversarial Conditions

This paper proposes the application of three key methods to multimodal neuroimaging data fusion. The first step is to classify neurodegenerative brain diseases in the considered scans from the available neuroimaging techniques. We propose to classify scans by selecting relevant disease detection features utilizing a gametheoretic approach and evidence combination. We applied a filtering feature selection based on a coalitional game. The second step is to aggregate the classifiers' outcomes by leveraging an improvement of the Dempster-Shafer combination rule obtained by applying evolutionary game theory to determine a final decision from the various classifiers' results, also considering the subjective doctor opinion. Last, the overall solution can be deployed in a distributed manner. The robustness of the interactions is achievable by modeling them as a signaling game to determine when rejecting those messages suspected of being malicious.