用Transformer融合动态影像数据，提升阿尔茨海默病生存预测准确率
Dynamic Survival Prediction using Longitudinal Images based on Transformer
- 基于Transformer架构，分三模块处理影像空间特征、时间序列信息与生存分析
- 在阿尔茨海默病数据上显著优于现有方法，能识别关键影像生物标志物
- 支持可解释性分析，适合临床研究与医学影像智能诊断场景
利用多时相医学影像进行生存分析在疾病早期检测与预后评估中具有重要意义，能提供超越单次影像评估的洞见。然而，现有方法常未能有效利用存在删失的数据，忽视多次随访影像间的相关性，且缺乏可解释性。本文提出SurLonFormer，一种基于Transformer的神经网络，将纵向医学影像与结构化数据结合用于生存预测。该模型包含三个核心组件：视觉编码器提取空间特征，序列编码器聚合时间信息，生存编码器基于Cox比例风险模型。该框架有效整合删失数据，解决可扩展性问题，并通过遮蔽敏感性分析增强可解释性，实现动态生存预测。大量模拟实验与阿尔茨海默病真实数据应用表明，SurLonFormer在预测性能上表现优异，成功识别出与疾病相关的影像生物标志物。
原文摘要 · Abstract (English)
Survival analysis utilizing multiple longitudinal medical images plays a pivotal role in the early detection and prognosis of diseases by providing insight beyond single-image evaluations. However, current methodologies often inadequately utilize censored data, overlook correlations among longitudinal images measured over multiple time points, and lack interpretability. We introduce SurLonFormer, a novel Transformer-based neural network that integrates longitudinal medical imaging with structured data for survival prediction. Our architecture comprises three key components: a Vision Encoder for extracting spatial features, a Sequence Encoder for aggregating temporal information, and a Survival Encoder based on the Cox proportional hazards model. This framework effectively incorporates censored data, addresses scalability issues, and enhances interpretability through occlusion sensitivity analysis and dynamic survival prediction. Extensive simulations and a real-world application in Alzheimer's disease analysis demonstrate that SurLonFormer achieves superior predictive performance and successfully identifies disease-related imaging biomarkers.
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