计算机科学
人工智能
神经影像学
模式
功能(生物学)
光学(聚焦)
深度学习
任务(项目管理)
对比度(视觉)
机器学习
自然语言处理
神经科学
心理学
物理
管理
进化生物学
社会学
光学
经济
生物
社会科学
作者
Kai Yang,Haoteng Tang,Siyuan Dai,Lei Guo,J.Y. Liu,Yalin Wang,Alex Leow,Paul M. Thompson,Heng Huang,Liang Zhan
标识
DOI:10.1007/978-3-031-43898-1_14
摘要
The modeling of the interaction between brain structure and function using deep learning techniques has yielded remarkable success in identifying potential biomarkers for different clinical phenotypes and brain diseases. However, most existing studies focus on one-way mapping, either projecting brain function to brain structure or inversely. This type of unidirectional mapping approach is limited by the fact that it treats the mapping as a one-way task and neglects the intrinsic unity between these two modalities. Moreover, when dealing with the same biological brain, mapping from structure to function and from function to structure yields dissimilar outcomes, highlighting the likelihood of bias in one-way mapping. To address this issue, we propose a novel bidirectional mapping model, named Bidirectional Mapping with Contrastive Learning (BMCL), to reduce the bias between these two unidirectional mappings via ROI-level contrastive learning. We evaluate our framework on clinical phenotype and neurodegenerative disease predictions using two publicly available datasets (HCP and OASIS). Our results demonstrate the superiority of BMCL compared to several state-of-the-art methods.
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