Transfer learning from typical Alzheimer's disease to rare dementias using Disease Knowledge Transfer


Population studies in rarer neurodegenerative diseases often suffer from limited datasets. While training sophisticated Machine Learning models on such data is difficult, transfer learning is a key potential solution. We use Disease Knowledge Transfer to infer robust multimodal biomarker trajectories in rare neurodegenerative diseases even when only limited, unimodal data is available, by transferring information from larger multimodal datasets of common neurodegenerative diseases. This allows the estimation of plausible, multimodal biomarker trajectories related to amyloid, tau, glucose metabolism and white-matter degradation in Posterior Cortical Atrophy given only unimodal MRI data.

The Deep Dementia Phenotyping (DEMON) Network