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Recent advancements in the acquisition of various brain data sources havecreated new opportunities for integrating multimodal brain data to assist inearly detection of complex brain disorders. However, current data integrationapproaches typically need a complete set of biomedical data modalities, whichmay not always be feasible, as some modalities are only available inlarge-scale research cohorts and are prohibitive to collect in routine clinicalpractice. Especially in studies of brain diseases, research cohorts may includeboth neuroimaging data and genetic data, but for practical clinical diagnosis,we often need to make disease predictions only based on neuroimages. As aresult, it is desired to design machine learning models which can use allavailable data (different data could provide complementary information) duringtraining but conduct inference using only the most common data modality. Wepropose a new incomplete multimodal data integration approach that employstransformers and generative adversarial networks to effectively exploitauxiliary modalities available during training in order to improve theperformance of a unimodal model at inference. We apply our new method topredict cognitive degeneration and disease outcomes using the multimodalimaging genetic data from Alzheimer's Disease Neuroimaging Initiative (ADNI)cohort. Experimental results demonstrate that our approach outperforms therelated machine learning and deep learning methods by a significant margin.

Incomplete Multimodal Learning for Complex Brain Disorders Prediction