基于结构和功能MR的多模态多层感知机模型提高精神分裂症分类率
Improving Classification Rate of Schizophrenia Using a Multimodal Multi-Layer Perceptron Model with Structural and Functional MR
摘要：
各种各样的脑成像技术使我们能够利用不同数据形式所固有的信息。多模态数据集的丰富性可以提高预测能力，并揭示潜在变量，否则将不会被发现。然而，多模态数据的分析通常是通过假设线性相互作用来进行的，这会影响结果的准确性。我们建议使用多模态多层感知器模型，以提高结构和功能磁共振成像（sMRI和fMRI）相结合的预测能力。我们还使用了一个合成数据生成器来预训练每个模态输入层，减轻了小样本量的影响，这通常是脑成像模态的情况。该模型将ROC曲线下面积的平均值和不确定性提高到0.850 ± 0.051，而单个模态的最佳结果为0.741 ± 0.075（sMRI）和0.833 ± 0.050（fMRI）。
英文摘要：
The wide variety of brain imaging technologies allows us to exploit information inherent to different data modalities. The richness of multimodal datasets may increase predictive power and reveal latent variables that otherwise would have not been found. However, the analysis of multimodal data is often conducted by assuming linear interactions which impact the accuracy of the results. We propose the use of a multimodal multi-layer perceptron model to enhance the predictive power of structural and functional magnetic resonance imaging (sMRI and fMRI) combined. We also use a synthetic data generator to pre-train each modality input layers, alleviating the effects of the small sample size that is often the case for brain imaging modalities. The proposed model improved the average and uncertainty of the area under the ROC curve to 0.850+-0.051 compared to the best results on individual modalities (0.741+-0.075 for sMRI, and 0.833+-0.050 for fMRI).
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