摘要
自闭症的诊断主要依靠患者的病史与临床症状表现,尚缺乏客观的评价指标,因此挖掘与疾病相关的生物标记,对于实现自闭症的早期识别与干预至关重要。尽管多中心脑影像数据增加了样本数量并提高了数据的统计能力,有助于提高自闭症的诊断性能,但目前的研究常受到数据异质性的困扰。为此本文提出基于低秩表示判别域适应的诊断模型,实现对多中心自闭症的预测分析。首先将源域数据和目标域数据映射到公共空间,并在空间用目标域数据对源域数据进行重新表示,从而降低源域和目标域之间的分布差异;其次通过学习正交重构矩阵使得源域数据在公共空间中的表示能够保留主要能量,从而适合于随后的学习任务;最后使用源域数据的标签信息将分类损失整合到训练过程中,从而保证公共空间表示的判别能力。为了求解所提出的模型,提出了基于交替方向乘子算法的优化策略。实验结果表明,该模型能够降低多中心数据分布差异,实现知识的有效迁移,从而提高多中心自闭症的诊断性能。
The diagnosis of autism spectrum disorder(ASD)mainly relies on the patient’s medical history and clinical symptoms,and there is still a lack of objective evaluation indicators.Therefore,the discovery of disease-related biomarkers is essential for early identification and intervention.Although the multi-site brain imaging data have increased the sample size and improved the statistical power,which helps to improve the diagnostic performance of autism,the current research is often plagued by data heterogeneity.To address this issue,a discriminative domain adaption via low-rank representation(DDA-LRR)framework for multi-site ASD identification is proposed.Specifically,we first transfer both source and target data to a common subspace,where each source data can be represented by a combination of source samples such that the distribution differences can be well relieved.Then,we learn an orthogonal reconstruction matrix,which can preserve the main energy in the obtained low-dimensional embedding space and thus is appropriate for the subsequent learning tasks.Finally,to ensure the discriminative ability of the low-rank representation,we use the label information of the source data to integrate the classification loss into the training stage.An efficient optimization strategy based on the alternating direction method of multipliers method is developed to solve the proposed DDA-LRR method.Experimental results show that the proposed method can reduce the differences in data distributions of multiple sites,realize the effective transfer of knowledge,and improve the diagnosis performance of multi-site ASD effectively.
作者
李习之
朱灵瑶
王明亮
LI Xizhi;ZHU Lingyao;WANG Mingliang(School of Computer and Software,Nanjing University of Information Science and Technology,Nanjing 210044,China;Engineering Research Center of Digital Forensics,Ministry of Education,Nanjing University of Information Science and Technology,Nanjing 210044,China;MIIT Key Laboratory of Pattern Analysis and Machine Intelligence,Nanjing University of Aeronautics&Astronautics,Nanjing 211106,China)
出处
《数据采集与处理》
CSCD
北大核心
2023年第4期886-897,共12页
Journal of Data Acquisition and Processing
基金
国家自然科学基金青年基金(62102188)
江苏省自然科学基金青年基金(BK20210647)
江苏省高等学校自然科学研究项目(21KJB520013)
中国博士后科学基金(2021M700076)
中央高校基本科研业务费资助项目(NJ2022028)
南京信息工程大学人才启动经费项目。
关键词
低秩表示
域适应
多中心
自闭症
疾病预测
low-rank representation
domain adaptation
multi-site
autism spectrum disorder(ASD)
disease prediction
