将扩散模型引入脑成像分析，提升疾病诊断与脑解码性能
Diffusion Models for Computational Neuroimaging: A Survey
- 用扩散模型处理脑影像数据，通过去噪机制生成高质量脑信号
- 在脑疾病诊断和神经解码任务中实现更精准的预测效果
- 适合神经科学与医学影像研究者参考最新技术进展
计算神经影像学旨在通过分析脑影像或脑信号，为人类认知与行为提供机制性洞察和预测工具。尽管扩散模型在自然图像生成中展现出稳定性和高质量，但其在脑数据上的应用日益受到关注，可用于数据增强、疾病诊断和脑解码等神经科学任务。本文综述了近期将扩散模型融入计算神经影像的研究进展。首先介绍常见的神经影像数据模态，随后阐述扩散模型的构建方式与条件控制机制。接着分析去噪起始点、条件输入和生成目标等变体如何针对特定神经影像任务进行优化。为全面呈现当前研究，我们公开维护一个项目仓库：https://github.com/JoeZhao527/dm4neuro。
原文摘要 · Abstract (English)
Computational neuroimaging involves analyzing brain images or signals to provide mechanistic insights and predictive tools for human cognition and behavior. While diffusion models have shown stability and high-quality generation in natural images, there is increasing interest in adapting them to analyze brain data for various neurological tasks such as data enhancement, disease diagnosis and brain decoding. This survey provides an overview of recent efforts to integrate diffusion models into computational neuroimaging. We begin by introducing the common neuroimaging data modalities, follow with the diffusion formulations and conditioning mechanisms. Then we discuss how the variations of the denoising starting point, condition input and generation target of diffusion models are developed and enhance specific neuroimaging tasks. For a comprehensive overview of the ongoing research, we provide a publicly available repository at https://github.com/JoeZhao527/dm4neuro.
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