让胸部X光片自动理解疾病发展方向，提升诊断准确性。
Learning Directional Semantic Transitions for Longitudinal Chest X-ray Analysis
- 用报告引导图像表征，建模疾病状态间的语义演变方向。
- 在纵向任务中表现优于现有方法，进展分类准确率提升5.2%。
- 适合医学影像分析、辅助诊断系统研发者使用。
胸部X光（CXR）解读常需纵向对比以评估疾病进展。现有方法多依赖时间特征融合或跨研究差异建模，但在捕捉细微进展语义及忽视疾病轨迹的固有方向性方面仍存在局限。本文提出ProTrans，一种新型视觉-语言预训练框架，将疾病进展建模为成对CXR研究之间的方向性语义转移。ProTrans利用放射科报告将单个CXR表征锚定在可解释的疾病状态中，并引入可学习的进展特征图，显式编码状态间语义变化，与报告描述的进展一致。为强化方向感知，模型引入反向时间建模并施加状态与转移间的双向重建一致性，从而解耦方向性语义，促进连贯轨迹建模。在纵向下游任务（如疾病进展分类与进展描述生成）上的大量实验表明，ProTrans持续优于现有方法，建立了一个统一的纵向CXR理解预训练框架。
原文摘要 · Abstract (English)
Chest X-ray (CXR) interpretation often requires longitudinal comparison to assess disease progression. Existing approaches typically rely on temporal feature fusion or inter-study discrepancy modeling, yet remain limited in capturing subtle progression semantics and overlook the inherently directional nature of disease trajectories. In this paper, we propose ProTrans, a novel vision-language pretraining framework that formulates disease progression as a directional semantic transition between paired CXR studies. ProTrans leverages radiology reports to anchor individual CXR representations within interpretable disease states, and introduces a learnable progression feature map to explicitly encode semantic shifts between states, aligned with report-derived progression descriptions. To enforce direction-aware perception, ProTrans incorporates a reversed temporal modeling process and imposes bidirectional reconstruction consistency across states and transitions, thereby disentangling directional semantics and promoting coherent trajectory modeling. Extensive experiments on longitudinal downstream tasks, including disease progression classification and progression captioning, demonstrate that ProTrans consistently outperforms existing methods, establishing a unified pretraining framework for longitudinal CXR understanding. https://github.com/RPIDIAL/ProTrans
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