Combining Images Across Multiple Subjects
跨多个受试者整合图像信息AI 解读 ↓
← 返回论文列表与筛选
阅读说明
本页整理公开书目信息，不生成学术质量评分。AI 解读根据题名及所注明的摘要或原文片段生成，未完成全文核验，可能有误；具体假设、证明与结论请核对原文。摘要保留来源文本，未经模型改写。
DOI 首次登记不等于接收或发表日期。部分更正、评论等条目没有独立摘要；缺少可靠材料时保留待补状态。
引用量采用 OpenAlex 单条关联记录，不合计多个记录；关联记录可能包含正式发表版本，不等于此版本单独引用。缺失不视为零，引用指标不代表论文质量。
关注作者按书目中的完整姓名匹配，同名和姓名变体需人工辨别。相关论文先按共享细分方向和具体方法排列，再参考通用方法与发表日期。
分类依据与纠错
AI 根据所提供的论文材料归类：摘要研究医学图像的贝叶斯层级整合，受试者间随机效应不足以支持面板或因子分类。
反馈分类问题
正在核对关联版本…
代码、数据与补充材料
正在核对对应版本的资源…
跨多个受试者整合图像信息
- 研究问题
- 如何整合多个受试者的稀疏二元脑功能观测，同时估计个体区域、总体区域及个体间差异，并预测新受试者的目标区域？
- 方法与思路
- 作者用简单几何模板混合构建贝叶斯层级模型，将个体模板作为随机效应，配合逻辑斯蒂观测模型和可逆跳跃 MCMC 推断。
- 材料陈述的贡献
- 摘要称，该框架可跨受试者借用信息、估计总体区域并预测未来个体；作者还提出图像差异度量，以脑功能数据展示方法。
- 阅读关注点
- 应核对模板数量与形状的先验、层级模型的可识别性、可逆跳跃算法的混合表现，以及新受试者预测如何验证。
- 阅读边界
- 仅据 OpenAlex 索引摘要，稿件版本未注明；摘要未量化借用信息的收益，也未说明预测验证结果，不能断言优于所有单个体方法。
原文摘要
This article introduces a Bayesian hierarchical model for combining information across multiple images. Our work was motivated by an invasive functional brain mapping technique called direct cortical electrical interference that gives a sparse set of binary observations of an underlying “true” region at multiple sites on the brain surface. To model region shapes that may vary widely across individuals, we use mixtures of simple templates, for example, circles. These subject-specific templates are treated as random effects, governed by a set of population templates that make up a population region. The numbers of subject-specific and population templates are treated as unknown variables to be estimated from the data. Conditional on the subject-specific regions, the observed data are modeled using logistic regression. To estimate the variability among images across patients, we develop a measure based on Baddeley's error measure for binary images. Because the dimension of the parameter space changes as the numbers of subject-specific and population templates change, inference is made using reversible jump Markov chain Monte Carlo. Using a hierarchical approach, we may better estimate each individual's region by borrowing strength from other subjects' data, we can estimate a population region by pooling information across subjects, and we can use a collection of data from previous patients to predict the location of a future patient's region of interest. The approach is illustrated with DCEI data collected on 20 patients for two motor tasks: tongue and hand movements.