X-WIN：通过预测性感知构建胸部X射线世界模型
X-WIN: Building Chest Radiograph World Model via Predictive Sensing
X-WIN 通过从 CT 体数据蒸馏三维解剖知识来构建 CXR 世界模型，其潜空间投影预测与亲和力引导对比对齐损失可迁移到其他需要从 2D 投影恢复 3D 结构的视觉任务，如 X 光片的三维重建与跨模态表征学习。
原文依据 we propose a novel CXR world model named X-WIN, which distills volumetric knowledge from chest computed tomography (CT) by learning to predict its 2D projections in latent space.
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论胸部X射线摄影（CXR）是疾病诊断中一种重要的医学影像技术。然而，作为二维投影图像，CXR受限于结构重叠，因而无法捕捉三维解剖结构。这一局限性使得表征学习和疾病诊断面临挑战。为解决该问题，我们提出了一种名为X-WIN的新型CXR世界模型，它通过在学习中预测胸部计算机断层扫描（CT）在潜空间中的二维投影，从CT中蒸馏体积知识。核心思想是，一个内化了三维解剖结构知识的世界模型能够预测CXR在三维空间中各种变换下的表现。在投影预测过程中，我们引入了一种亲和力引导的对比对齐损失，利用相互相似性来捕捉来自同一体积的投影之间丰富的相关信息。为提高模型适应性，我们通过掩码图像建模将真实CXR纳入训练，并采用域分类器促使真实与模拟CXR的表征在统计上相似。大量实验表明，X-WIN在使用线性探测和少样本微调的各种下游任务上优于现有基础模型。X-WIN还展示了渲染二维投影以重建三维CT体积的能力。
英文原文摘要 1321 字符
Chest X-ray radiography (CXR) is an essential medical imaging technique for disease diagnosis. However, as 2D projectional images, CXRs are limited by structural superposition and hence fail to capture 3D anatomies. This limitation makes representation learning and disease diagnosis challenging. To address this challenge, we propose a novel CXR world model named X-WIN, which distills volumetric knowledge from chest computed tomography (CT) by learning to predict its 2D projections in latent space. The core idea is that a world model with internalized knowledge of 3D anatomical structure can predict CXRs under various transformations in 3D space. During projection prediction, we introduce an affinity-guided contrastive alignment loss that leverages mutual similarities to capture rich, correlated information across projections from the same volume. To improve model adaptability, we incorporate real CXRs into training through masked image modeling and employ a domain classifier to encourage statistically similar representations for real and simulated CXRs. Comprehensive experiments show that X-WIN outperforms existing foundation models on diverse downstream tasks using linear probing and few-shot fine-tuning. X-WIN also demonstrates the ability to render 2D projections for reconstructing a 3D CT volume.