Incomplete Multimodal Learning for Complex Brain Disorders Prediction
Reza Shirkavand Liang Zhan Heng Huang Li Shen Paul M. Thompson
Recent advancements in the acquisition of various brain data sources have
created new opportunities for integrating multimodal brain data to assist in
early detection of complex brain disorders. However, current data integration
approaches typically need a complete set of biomedical data modalities, which
may not always be feasible, as some modalities are only available in
large-scale research cohorts and are prohibitive to collect in routine clinical
practice. Especially in studies of brain diseases, research cohorts may include
both neuroimaging data and genetic data, but for practical clinical diagnosis,
we often need to make disease predictions only based on neuroimages. As a
result, it is desired to design machine learning models which can use all
available data (different data could provide complementary information) during
training but conduct inference using only the most common data modality. We
propose a new incomplete multimodal data integration approach that employs
transformers and generative adversarial networks to effectively exploit
auxiliary modalities available during training in order to improve the
performance of a unimodal model at inference. We apply our new method to
predict cognitive degeneration and disease outcomes using the multimodal
imaging genetic data from Alzheimer's Disease Neuroimaging Initiative (ADNI)
cohort. Experimental results demonstrate that our approach outperforms the
related machine learning and deep learning methods by a significant margin.