Classical and Deep Learning Approaches to Longitudinal Modeling in Neuroimages - CGSI 2024

Explore classical and deep learning approaches to longitudinal modeling in neuroimages through this 45-minute conference talk by Daniel Tward at the Computational Genomics Summer Institute (CGSI) 2024. Delve into the latest research on longitudinal diffeomorphometry for detecting atrophy in mild cognitive impairment, focusing on entorhinal and transentorhinal regions. Examine innovative techniques such as longitudinal variational autoencoders for modeling imaging data progression and degenerative adversarial neuroimage nets for simulating disease progression. Gain insights into cutting-edge methodologies that combine traditional and machine learning approaches to analyze and predict changes in brain structure over time, with potential applications in Alzheimer's disease research and other neurodegenerative disorders.

Daniel Tward | Classical and Deep Learning Approaches to Longitudinal Modeling in ... | CGSI 2024