从脑功能连接网络监督中学习3D医学图像模型用于精神障碍诊断
Learning 3D Medical Image Models From Brain Functional Connectivity Network Supervision For Mental Disorder Diagnosis
- University of Science and Technology of China（中国科学技术大学）
- Institute of Artificial Intelligence, Hefei Comprehensive National Science Center（合肥综合性国家科学中心人工智能研究院）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
提出CINP框架，通过对比学习将功能连接网络知识迁移至结构MRI，并引入网络提示策略，实现仅用sMRI及少量FCN即可高效诊断精神障碍。
AI中文摘要：
在基于MRI的精神障碍诊断中，以往研究多关注功能MRI（fMRI）衍生的功能连接网络（FCN）。然而，标注fMRI数据集规模较小，限制了其广泛应用。同时，结构MRI（sMRI），如临床常用且易于获取的3D T1加权（T1w）MRI，常被忽视。为整合功能与结构的互补信息以提高诊断准确率，我们提出CINP（Contrastive Image-Network Pre-training），一个在sMRI与FCN间采用对比学习的框架。在预训练阶段，我们引入掩蔽图像建模和网络-图像匹配，以增强视觉表示学习与模态对齐。由于CINP促进知识从FCN向sMRI转移，我们引入网络提示。该方法仅使用疑似患者的sMRI及少量来自不同患者类别的FCN进行精神障碍诊断，这在真实临床场景中具有实用性。在三个精神障碍诊断任务上的竞争性表现证明了CINP在整合多模态MRI信息方面的有效性，以及通过网络提示将sMRI纳入临床诊断的潜力。
英文摘要：
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.
