Class activation attention transfer neural networks for MCI conversion prediction

Accurate prediction of the trajectory of Alzheimer's disease (AD) from an early stage is of substantial value for treatment and planning to delay the onset of AD. We propose a novel attention transfer method to train a 3D convolutional neural network to predict which patients with mild cognitive impairment (MCI) will progress to AD within 3 years. A model is first trained on a separate but related source task (task we are transferring information from) to automatically learn regions of interest (ROI) from a given image. Next we train a model to simultaneously classify progressive MCI (pMCI) and stable MCI (sMCI) (the target task we want to solve) and the ROIs learned from the source task. The predicted ROIs are then used to focus the model's attention on certain areas of the brain when classifying pMCI versus sMCI. Thus, in contrast to traditional transfer learning, we transfer attention maps instead of transferring model weights from a source task to the target classification task. Our Method outperformed all methods tested including traditional transfer learning and methods that used expert knowledge to define ROI. Furthermore, the attention map transferred from the source task highlights known Alzheimer's pathology.

Automated summary

We propose a novel attention transfer method for accurately predicting the progression of Alzheimer's disease (AD) in patients with mild cognitive impairment (MCI). Our method trains a 3D convolutional neural network to automatically learn regions of interest (ROI) from images and transfer attention maps instead of model weights. Our method outperformed traditional transfer learning and methods using expert knowledge to define ROI, and the attention map revealed Alzheimer's pathology.