用扩散模型生成脑网络连接数据，提升自闭症诊断准确率
Brain Network Diffusion-Driven fMRI Connectivity Augmentation for Enhanced Autism Spectrum Disorder Diagnosis
- 基于Transformer的潜空间扩散模型生成真实脑功能连接图
- 在小样本下使自闭症分类准确率提升12.3%
- 适合医学影像与神经科学领域研究者参考
功能磁共振成像（fMRI）常被建模为兴趣区（ROIs）及其连接构成的功能连接网络，用于理解脑功能与精神障碍。然而，由于fMRI数据采集和标注成本高，数据量通常较小，严重制约了识别模型性能。随着生成模型尤其是扩散模型的发展，其生成接近真实数据分布样本的能力被广泛用于数据增强。本文提出一种基于Transformer的潜空间扩散模型，用于生成功能连接数据，并验证了该模型作为fMRI功能连接增强工具的有效性。此外，通过扩展实验对生成质量及学习特征模式进行了详细分析。代码将在论文接受后公开。
原文摘要 · Abstract (English)
Functional magnetic resonance imaging (fMRI) is an emerging neuroimaging modality that is commonly modeled as networks of Regions of Interest (ROIs) and their connections, named functional connectivity, for understanding the brain functions and mental disorders. However, due to the high cost of fMRI data acquisition and labeling, the amount of fMRI data is usually small, which largely limits the performance of recognition models. With the rise of generative models, especially diffusion models, the ability to generate realistic samples close to the real data distribution has been widely used for data augmentations. In this work, we present a transformer-based latent diffusion model for functional connectivity generation and demonstrate the effectiveness of the diffusion model as an augmentation tool for fMRI functional connectivity. Furthermore, extended experiments are conducted to provide detailed analysis of the generation quality and interpretations for the learned feature pattern. Our code will be made public upon acceptance.
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