Probabilistic learning approaches for complex disease progression based on high-dimensional MRI data

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This project proposes informed, data-driven methods to reveal pathological trajectories based on high-dimensional medical data obtained from magnetic resonance imaging (MRI), which are relevant as both inputs and outputs in regression equations to adequately perform early diagnosis and to model, understand, and predict actual and future disease progression. For this, we will fuse deep learning (DL) methods with Bayesian statistics to (1) accurately predict the complete outcome distributions of individual patients based on MRI data and further confounders and covariates (such as clinical or demographical variables) to adequately quantify uncertainty in predictions in contrast to point predictions not delivering any measures of confidence (2) model temporal dynamics in biomedical patient data. Regarding (1), we will develop deep distributional regression models for image inputs to accurately predict the entire distributions of the different disease scores (e.g. symptom severity), which can be multivariate and are typically highly non-normally distributed. Regarding (2), we will model the complex temporal evolution in neurological diseases by developing DL-based state-space models. Neither model is tailored to a specific disease, but both will be exemplary developed and tested for two neurological diseases, namely Alzheimer’s disease (AD) and multiple sclerosis (MS), chosen for their different disease progression profiles.