FLARe: Forecasting by Learning Anticipated Representations

17 Apr 2019Surya Teja DevarakondaJoie Yeahuay WuYi Ren FungMadalina Fiterau

Computational models that forecast the progression of Alzheimer's disease at the patient level are extremely useful tools for identifying high risk cohorts for early intervention and treatment planning. The state-of-the-art work in this area proposes models that forecast by using latent representations extracted from the longitudinal data across multiple modalities, including volumetric information extracted from medical scans and demographic info... (read more)

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