发表机构
Oslo University Hospital（奥斯陆大学医院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对纵向医学影像随机划分导致的协变量偏移和采样失衡问题，提出三方数据集分析框架，结合肘部优化K-means与分层抽样，实现队列平衡，大幅降低强度偏差，提升划分稳定性。
AI 中文摘要
严格的数据集划分是纵向医学影像领域可靠深度学习的基础，但却常被忽视。对小型临床队列进行简单随机划分，常会在训练集、验证集和测试集之间引入协变量偏移和时间采样不平衡，使下游模型面临分布外评估的风险。针对这一漏洞，我们提出了可审计的三方数据集分析框架（Tripartite Dataset Analytics Framework），该框架可系统表征空间网格完整性、多参数强度指纹以及纵向时间轨迹，量化真实世界临床队列典型的重尾特征离散度和不规则的间歇性采样间隔。基于上述表征，我们提出了一种无监督时空队列平衡标准操作流程（SOP），该流程结合了在标准化六维联合强度-时间特征空间上经肘部法则优化的K-means聚类，以及簇内比例分层抽样。在包含149个样本的纵向对比增强T1加权脑MRI队列上，该方案将跨子集最大强度偏差从传统随机划分下的34.1%降至2.1%以下，同时使纵向随访间隔与总体均值高度对齐。在10个随机种子和3种划分配置下进行的蒙特卡洛压力测试证实，该对齐效果保持紧密约束，与随机划分的显著变异性形成鲜明对比。该方案为可变长度纵向临床影像工作流程中的队列工程提供了可复现、可推广的方法。
英文摘要
Rigorous dataset partitioning is a foundational, yet frequently overlooked, prerequisite for reliable deep learning in longitudinal medical imaging. Naively shuffling small clinical cohorts routinely introduces covariate shifts and temporal sampling imbalances across training, validation, and test subsets, exposing downstream models to out-of-distribution evaluation. We address this vulnerability with an auditable Tripartite Dataset Analytics Framework that systematically characterizes spatial grid integrity, multi-parametric intensity fingerprints, and longitudinal temporal trajectories, quantifying the heavy-tailed feature dispersion and irregular, episodic sampling intervals typical of real-world clinical cohorts. Building on this characterization, we formalize an unsupervised spatio-temporal cohort-balancing standard operating procedure (SOP) that combines elbow-optimized K-means clustering over a standardized, six-dimensional joint intensity-temporal feature space with intra-cluster proportionate stratified sampling. On a longitudinal, contrast-enhanced $T1$-weighted brain MRI cohort (N=149), the protocol reduces the maximum cross-subset intensity bias from 34.1% under conventional random shuffling to under 2.1%, while aligning longitudinal follow-up intervals closely around the population mean. Monte Carlo stress testing across ten random seeds and three split configurations confirms that this alignment remains tightly bounded, in clear contrast to the substantial variability of random partitioning. The resulting protocol offers a reproducible, generalizable procedure for cohort engineering in variable-length longitudinal clinical imaging workflows.
Comments22 pages, 7 figues
