Multi-taskLearning and Ensemble Approach to Predict Cognitive Scores for Patients with Alzheimer's Disease

Abstract

During its chronic degenerative course, Alzheimers Disease severely harms the patients cognitive abilities. Assessment of current and future cognition is an integral component of a diagnosis of dementia, and therefore an important clinical and scientific goal. Unfortunately, subjective, time-consuming and operator-sensitive clinical surveys or neuropyschiatric batteries remain the only viable methods of assessing cognition. Given that MRI is the most prevalent, cost-effective, and clinically important imaging modality, it may be considered a suitable predictor of cognition. Yet, it has hitherto proved very challenging to predict one from the other. We propose that an image-based Deep Learning model can be custom-built to achieve this goal. We designed a novel multi-task UNet model to predict the subjects current and future cognition (via ADAS-Cog scores), taking as input baseline T1-weighted MRI and demographic risk factors. The key innovation in the model is that it seeks to solve two adjacent but relevant tasks: image segmentation into tissue types; and prediction of cognition. The first task gives a high-accuracy brain segmentation, comparable to other cutting edge methods. The features trained from the segmentation task are used in the cognition task. This combination is far superior to stand-alone single-shot cognition models. We achieved excellent accuracy in both baseline and time-series forecast of ADAS-Cog scores. Through further feature map analysis made on the receptive fields, we managed to impart much-needed model interpretability, critical for real-world clinical practice. This study constitutes the best-reported performance of any comparable approach, and opens the door towards machine-based tracking of AD progression.