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Deep learning (DL) has been used in the automatic diagnosis of Mild CognitiveImpairment (MCI) and Alzheimer's Disease (AD) with brain imaging data. However,previous methods have not fully exploited the relation between brain image andclinical information that is widely adopted by experts in practice. To exploitthe heterogeneous features from imaging and tabular data simultaneously, wepropose the Visual-Attribute Prompt Learning-based Transformer (VAP-Former), atransformer-based network that efficiently extracts and fuses the multi-modalfeatures with prompt fine-tuning. Furthermore, we propose a Prompt fine-Tuning(PT) scheme to transfer the knowledge from AD prediction task for progressiveMCI (pMCI) diagnosis. In details, we first pre-train the VAP-Former withoutprompts on the AD diagnosis task and then fine-tune the model on the pMCIdetection task with PT, which only needs to optimize a small amount ofparameters while keeping the backbone frozen. Next, we propose a novel globalprompt token for the visual prompts to provide global guidance to themulti-modal representations. Extensive experiments not only show thesuperiority of our method compared with the state-of-the-art methods in pMCIprediction but also demonstrate that the global prompt can make the promptlearning process more effective and stable. Interestingly, the proposed promptlearning model even outperforms the fully fine-tuning baseline on transferringthe knowledge from AD to pMCI.

Visual-Attribute Prompt Learning for Progressive Mild Cognitive Impairment Prediction