Knowledge Commons of Institute of Automation,CAS
A Cross-Feature Mutual Learning Framework to Integrate Functional Connectivity and Activity for Brain Disorder Classification | |
Min Zhao1,2,3![]() ![]() ![]() ![]() | |
2024-05 | |
会议名称 | IEEE Engineering in Medicine and Biology Society |
会议日期 | July 15-19, 2024 |
会议地点 | USA |
摘要 | Time courses (TC) and functional network connectivity (FNC) features, derived from functional magnetic resonance imaging, show considerable potential in the study of brain disorders. Despite significant advancements, most deep learning approaches tend to either directly concatenate complementary MRI features at the input level or ensemble decisions after separately learning each feature, whereas an end-to-end, mixed feature learning framework is still lacking. To bridge this gap, we introduce a cross-feature mutual learning (CFML) to enable collaborative learning of TC-specific and FNC-specific models and facilitate mutual knowledge transfer to distill shared and robust characteristics from the high-level representations of TC and FNC, thereby enhancing brain disorder classification performance. Specifically, we first develop a recurrent neural network-based TC-specific encoder to capture temporal dynamic dependencies within TCs, alongside a transformer-based FNC-specific encoder to discern global high-order functional dependencies among independent components in FNCs. Subsequently, we design a cross-modal module for the adaptive integration of TC-specific and FNC-specific features. Additionally, the CFML strategy is proposed to collaboratively train these modules, incorporating feature-specific loss, feature-exchange loss, and joint loss. Empirical results reveal that CFML achieves an accuracy of 85.1% in differentiating healthy controls (HC) from schizophrenia (SZ) patients, surpassing 12 comparative models by a margin of 3.0-9.2% accuracy using either static FNC or TCs or both. These findings underscore the efficacy of CFML in classifying brain disorders, highlighting its potential in advancing this field. |
收录类别 | EI |
是否为代表性论文 | 是 |
七大方向——子方向分类 | 医学影像处理与分析 |
国重实验室规划方向分类 | 其他 |
是否有论文关联数据集需要存交 | 否 |
文献类型 | 会议论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/57408 |
专题 | 脑图谱与类脑智能实验室_脑网络组研究 |
通讯作者 | Jing Sui |
作者单位 | 1.Brainnetome Center, Institute of Automation, Chinese Academy of Sciences 2.School of Artificial Intelligence, University of Chinese Academy of Sciences 3.State Key Laboratory of Cognitive Neuroscience, Beijing Normal University, Beijing, China 4.Laboratory of Brain Atlas and Brain-inspired Intelligence, Institute of Automation, Chinese Academy of Sciences, Beijing, China 5.Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS) Center, Georgia State University, Atlanta, GA, USA |
第一作者单位 | 中国科学院自动化研究所 |
推荐引用方式 GB/T 7714 | Min Zhao,Rongtao Xu,Dongmei Zhi,et al. A Cross-Feature Mutual Learning Framework to Integrate Functional Connectivity and Activity for Brain Disorder Classification[C],2024. |
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文件名称/大小 | 文献类型 | 版本类型 | 开放类型 | 使用许可 | ||
2024EMBC_final.pdf(2565KB) | 会议论文 | 开放获取 | CC BY-NC-SA | 浏览 下载 |
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