标题： GLOMIA-Pro：一种可推广的纵向医学图像分析框架用于疾病进展预测 显示英文标题

标题： GLOMIA-Pro: A Generalizable Longitudinal Medical Image Analysis Framework for Disease Progression Prediction

摘要： 纵向医学图像对于通过捕捉与动态生物过程相关的时空变化来监测疾病进展至关重要。 尽管当前方法在建模时空模式方面取得了进展，但它们面临三个关键限制：(1) 缺乏适用于多种疾病进展预测任务的通用框架；(2) 频繁忽视疾病分期中固有的序数性质；(3) 由于相邻时间点之间的结构相似性，容易出现表示崩溃，这可能会掩盖细微但具有区分性的进展生物标志物。 为了解决这些限制，我们提出了一种适用于疾病进展预测的通用纵向医学图像分析框架（GLOMIA-Pro）。 GLOMIA-Pro包括两个核心组件：进展表示提取和进展感知融合。 进展表示提取模块引入了一种分段正交注意力机制，并采用了一种新的序数进展约束，以分离与疾病进展相关的细粒度时间影像变化。 进展感知融合模块结合了一种重新设计的跳跃连接架构，该架构将学习到的进展表示与当前影像表示相结合，在跨时间融合过程中有效缓解表示崩溃。 在两个不同的临床应用中进行了验证：膝骨关节炎严重程度预测和食管癌治疗反应评估，GLOMIA-Pro始终优于七种最先进的纵向分析方法。 消融研究进一步证实了各个组件的贡献，展示了GLOMIA-Pro在各种临床场景中的鲁棒性和通用性。 显示英文摘要

摘要： Longitudinal medical images are essential for monitoring disease progression by capturing spatiotemporal changes associated with dynamic biological processes. While current methods have made progress in modeling spatiotemporal patterns, they face three key limitations: (1) lack of generalizable framework applicable to diverse disease progression prediction tasks; (2) frequent overlook of the ordinal nature inherent in disease staging; (3) susceptibility to representation collapse due to structural similarities between adjacent time points, which can obscure subtle but discriminative progression biomarkers. To address these limitations, we propose a Generalizable LOngitudinal Medical Image Analysis framework for disease Progression prediction (GLOMIA-Pro). GLOMIA-Pro consists of two core components: progression representation extraction and progression-aware fusion. The progression representation extraction module introduces a piecewise orthogonal attention mechanism and employs a novel ordinal progression constraint to disentangle finegrained temporal imaging variations relevant to disease progression. The progression-aware fusion module incorporates a redesigned skip connection architecture which integrates the learned progression representation with current imaging representation, effectively mitigating representation collapse during cross-temporal fusion. Validated on two distinct clinical applications: knee osteoarthritis severity prediction and esophageal cancer treatment response assessment, GLOMIA-Pro consistently outperforms seven state-of-the-art longitudinal analysis methods. Ablation studies further confirm the contribution of individual components, demonstrating the robustness and generalizability of GLOMIA-Pro across diverse clinical scenarios.