用2D胸片重建3D结构，提升疾病诊断精度
Xray2Xray: World Model from Chest X-rays with Volumetric Context
- 通过多角度投影动态建模，从2D胸片学习3D体积表征
- 在心血管风险预测上超越监督与自监督方法
- 适合医学影像分析、三维重建方向的研究者
胸部X光片（CXRs）是应用最广泛的医学影像手段，在疾病诊断中起关键作用。然而作为二维投影图像，其存在结构重叠问题，限制了精确诊断和风险预测能力。为此，本文提出Xray2Xray，一种基于胸片的新型世界模型，能够从二维胸片中学习编码三维结构信息的隐式表征。该模型通过视觉模型与转移模型联合建模不同投影视角下X光片的动态变化，捕捉胸腔体积的潜在表示。我们利用Xray2Xray的隐式表征在下游任务中进行风险预测与疾病分类。实验表明，该模型在心血管疾病风险评估中优于监督学习与自监督预训练方法，并在五类病理分类任务中达到竞争性表现。此外，通过生成任务评估了隐式表征质量，证明其可用于重建三维体结构上下文。
原文摘要 · Abstract (English)
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.
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