  Algorithmic image-based diagnosis and prognosis of neurodegenerative diseases
on longitudinal data has drawn great interest from computer vision researchers.
The current state-of-the-art models for many image classification tasks are
based on the Convolutional Neural Networks (CNN). However, a key challenge in
applying CNN to biological problems is that the available labeled training
samples are very limited. Another issue for CNN to be applied in computer aided
diagnosis applications is that to achieve better diagnosis and prognosis
accuracy, one usually has to deal with the longitudinal dataset, i.e., the
dataset of images scanned at different time points. Here we argue that an
enhanced CNN model with transfer learning for the joint analysis of tasks from
multiple time points or regions of interests may have a potential to improve
the accuracy of computer aided diagnosis. To reach this goal, we innovate a CNN
based deep learning multi-task dictionary learning framework to address the
above challenges. Firstly, we pre-train CNN on the ImageNet dataset and
transfer the knowledge from the pre-trained model to the medical imaging
progression representation, generating the features for different tasks. Then,
we propose a novel unsupervised learning method, termed Multi-task Stochastic
Coordinate Coding (MSCC), for learning different tasks by using shared and
individual dictionaries and generating the sparse features required to predict
the future cognitive clinical scores. We apply our new model in a publicly
available neuroimaging cohort to predict clinical measures with two different
feature sets and compare them with seven other state-of-the-art methods. The
experimental results show our proposed method achieved superior results.
