用混合深度学习模型识别自闭症脑结构标志物
Identifying Autism-Related Neurobiomarkers Using Hybrid Deep Learning Models
- 3D CNN提取脑影像特征，SVM完成分类
- 准确率达87.6%，关键区域集中在皮层边界和中线区
- 可视化结果与已有自闭症研究高度吻合
自闭症谱系障碍（ASD）与皮层及皮层下区域的结构改变相关。定量神经影像学可实现对这些神经解剖模式的大规模分析。本研究使用公开的ABIDE I数据集（n=1,112）中的结构磁共振成像（T1加权）数据，通过混合模型对自闭症患者与健康对照进行分类。首先训练3D卷积神经网络（CNN）以学习神经解剖特征表示，再将特征输入支持向量机（SVM）进行最终分类。采用梯度加权类激活映射（Grad-CAM）对CNN进行可视化，揭示模型预测中贡献最大的脑区。结果显示，差异图在皮层边界区域最为显著，同时在中线额-颞-顶叶区域有额外强调，整体与既往自闭症神经影像学发现一致。
原文摘要 · Abstract (English)
Autism spectrum disorder (ASD) has been associated with structural alterations across cortical and subcortical regions. Quantitative neuroimaging enables large-scale analysis of these neuroanatomical patterns. This project used structural MRI (T1-weighted) data from the publicly available ABIDE I dataset (n = 1,112) to classify ASD and control participants using a hybrid model. A 3D convolutional neural network (CNN) was trained to learn neuroanatomical feature representations, which were then passed to a support vector machine (SVM) for final classification. Gradient-weighted class activation mapping (Grad-CAM) was applied to the CNN to visualize the brain regions that contributed most to the model predictions. The Grad-CAM difference maps showed strongest relevance along cortical boundary regions, with additional emphasis in midline frontal-temporal-parietal areas, which is broadly consistent with prior ASD neuroimaging findings.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。