A Weakly Supervised Deep Learning Model for Alzheimer’s Disease Prognosis Using MRI and Incomplete Labels

Predicting cognitive scores using magnetic resonance imaging (MRI) can aid in the early recognition of Alzheimer’s disease (AD) and provide insights into future disease progression. Existing methods typically ignore the temporal consistency of cognitive scores and discard the subjects with incomplete cognitive scores. In this paper, we propose a Weakly supervised Alzheimer’s Disease Prognosis (WADP) model that incorporates an image embedding network and a label embedding network to predict cognitive scores using baseline MRI and incomplete cognitive scores. The image embedding network is an attention consistency regularized network to project MRI into the image embedding space and output the cognitive scores at multiple time-points. The attention consistency regularization captures the correlations among time-points by encouraging the attention maps at different time-points to be similar. The label embedding network employs a denoising autoencoder to embed cognitive scores into the label embedding space and impute missing cognitive scores. This enables the utilization of subjects with incomplete cognitive scores in the training process. Moreover, a relation alignment module is incorporated to make the relationships between samples in the image embedding space consistent with those in the label embedding space. The experimental results on two ADNI datasets show that WADP outperforms the state-of-the-art methods.