Class activation attention transfer neural networks for MCI conversion prediction. (April 2023)

• Record Type: Journal Article
• Authors: Luo, Min; He, Zhen; Cui, Hui; Chen, Yi-Ping Phoebe; Ward, Phillip
• Is Part Of: Computers in biology and medicine. Volume 156(2023)
• Publication Date: 2023-04
• Subjects: Alzheimer's disease -- Mild Cognitive impairment -- Prediction -- Class activation maps -- Convolutional neural networks -- Attention mechanism; Medicine -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; 610.285
• DOI: 10.1016/j.compbiomed.2023.106700
• Languages: English
• ISSNs: 0010-4825

Abstract

Abstract: 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. Highlights: A novel attention transfer method for predicting MCI conversion to Alzheimer's disease. Proposed attention transfer method outperforms traditional transfer learning. Proposed method outperforms methods that use expert knowledge to define regions of interest. The attention map transferred from the source task highlights known Alzheimer's pathology.