Multi-task multi-level feature adversarial network for joint Alzheimer’s disease diagnosis and atrophy localization using sMRI

Capitalizing on structural magnetic resonance imaging (sMRI), existing deep learning methods (especially convolutional neural networks, CNNs) have been widely and successfully applied to computer-aided diagnosis of Alzheimer's disease (AD) and its prodromal stage (i.e. mild cognitive impairment, MCI). But considering the generalization capability of the obtained model trained on limited number of samples, we construct a multi-task multi-level feature adversarial network (M2FAN) for joint diagnosis and atrophy localization using baseline sMRI. Specifically, the linear-aligned T1 MR images were first processed by a lightweight CNN backbone to capture the shared intermediate feature representations, which were then branched into a global subnet for preliminary dementia diagnosis and a multi instance learning network for brain atrophy localization in multi-task learning manner. As the global discriminative information captured by the global subnet might be unstable for disease diagnosis, we further designed a module of multi-level feature adversarial learning that accounts for regularization to make global features robust against the adversarial perturbation synthesized by the local/instance features to improve the diagnostic performance. Our proposed method was evaluated on three public datasets (i.e. ADNI-1, ADNI-2, and AIBL), demonstrating competitive performance compared with several state-of-the-art methods in both tasks of AD diagnosis and MCI conversion prediction.