Training recurrent neural networks robust to incomplete data: application to Alzheimer ’s disease progression modeling

Alzheimer ’s disease (AD) is a chronic neurodegenerative disorder that begins with memory loss and develops over time, causing issues in conversation, orientation, and control of bodily functions (McKhann et al., 1984). Early diagnosis of the disease is challenging and is usually made once cognitive impair ment has already compromised daily living. Hence, developing robust, data-driven methods for disease progression modeling (DPM) utilizing longitudinal data is necessary to yield a complete perspective on the disease for better diagnosis, monitoring, and prognosis (Oxtoby and Alexander, 2017).

Source: Medical Image Analysis - January 11, 2019 Category: Radiology Authors: Mostafa Mehdipour Ghazi, Mads Nielsen, Akshay Pai, M. Jorge Cardoso, Marc Modat, S ébastien Ourselin, Lauge Sørensen Source Type: research