标题： Xray2Xray：基于体积上下文的胸部X光世界模型 显示英文标题

标题： Xray2Xray: World Model from Chest X-rays with Volumetric Context

摘要： 胸部X光片（CXRs）是最常用的医学影像方式，在疾病诊断中起着关键作用。 然而，作为二维投影图像，CXRs受到结构重叠的限制，这限制了它们在精确疾病诊断和风险预测中的效果。 为了解决二维CXRs的局限性，本研究引入了Xray2Xray，这是一种新颖的世界模型，能够从胸部X光片中学习编码三维结构信息的潜在表示。 Xray2Xray通过使用视觉模型和转换模型对不同角度位置的X光投影的转换动态进行建模，从而捕捉胸部体积的潜在表示。 我们使用Xray2Xray的潜在表示进行下游的风险预测和疾病诊断任务。 实验结果表明，Xray2Xray在心血管疾病风险评估方面优于监督方法和自监督预训练方法，并在分类五种病理的CXRs中取得了具有竞争力的性能。 我们还通过合成任务评估了Xray2Xray潜在表示的质量，并证明潜在表示可用于重建体积上下文。 显示英文摘要

摘要： Chest X-rays (CXRs) are the most widely used medical imaging modality and play a pivotal role in diagnosing diseases. However, as 2D projection images, CXRs are limited by structural superposition, which constrains their effectiveness in precise disease diagnosis and risk prediction. To address the limitations of 2D CXRs, this study introduces Xray2Xray, a novel World Model that learns latent representations encoding 3D structural information from chest X-rays. Xray2Xray captures the latent representations of the chest volume by modeling the transition dynamics of X-ray projections across different angular positions with a vision model and a transition model. We employed the latent representations of Xray2Xray for downstream risk prediction and disease diagnosis tasks. Experimental results showed that Xray2Xray outperformed both supervised methods and self-supervised pretraining methods for cardiovascular disease risk estimation and achieved competitive performance in classifying five pathologies in CXRs. We also assessed the quality of Xray2Xray's latent representations through synthesis tasks and demonstrated that the latent representations can be used to reconstruct volumetric context.