用结构脑图与功能网络对比学习，提升精神疾病诊断准确率
Learning 3D Medical Image Models From Brain Functional Connectivity Network Supervision For Mental Disorder Diagnosis
- 通过对比结构脑图与功能网络进行自监督预训练
- 在三个精神疾病任务上达到领先性能，小样本下仍有效
- 仅需少量功能网络数据，适合临床实际应用
在基于MRI的精神疾病诊断中，以往研究多聚焦于功能磁共振成像（fMRI）提取的功能连接网络（FCN）。然而，标注的fMRI数据集规模小，限制了其广泛应用。相比之下，临床中普遍且易获取的3D T1加权结构磁共振成像（sMRI）常被忽视。为融合功能与结构信息以提升诊断精度，我们提出CINP（对比图像-网络预训练）框架，通过sMRI与FCN之间的对比学习实现。预训练阶段引入掩码图像建模和网络-图像匹配，增强视觉表征学习与模态对齐。由于CINP能将功能网络知识迁移至结构脑图，我们进一步设计网络提示机制：仅需疑似患者结构脑图及少量来自不同病类的功能网络，即可完成诊断，契合真实临床场景。在三个精神疾病诊断任务上的表现证明，CINP有效整合多模态MRI信息，展现了将结构脑图纳入临床诊断的潜力。
原文摘要 · Abstract (English)
In MRI-based mental disorder diagnosis, most previous studies focus on functional connectivity network (FCN) derived from functional MRI (fMRI). However, the small size of annotated fMRI datasets restricts its wide application. Meanwhile, structural MRIs (sMRIs), such as 3D T1-weighted (T1w) MRI, which are commonly used and readily accessible in clinical settings, are often overlooked. To integrate the complementary information from both function and structure for improved diagnostic accuracy, we propose CINP (Contrastive Image-Network Pre-training), a framework that employs contrastive learning between sMRI and FCN. During pre-training, we incorporate masked image modeling and network-image matching to enhance visual representation learning and modality alignment. Since the CINP facilitates knowledge transfer from FCN to sMRI, we introduce network prompting. It utilizes only sMRI from suspected patients and a small amount of FCNs from different patient classes for diagnosing mental disorders, which is practical in real-world clinical scenario. The competitive performance on three mental disorder diagnosis tasks demonstrate the effectiveness of the CINP in integrating multimodal MRI information, as well as the potential of incorporating sMRI into clinical diagnosis using network prompting.
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