PaCX-MAE: 生理增强的胸部X光掩码自编码器
PaCX-MAE: Physiology-Augmented Chest X-Ray Masked Autoencoder
AI总结：
提出PaCX-MAE跨模态蒸馏框架，通过双对比预测目标将生理先验注入胸部X光编码器，在保持单模态推理的同时提升生理相关任务性能。
摘要：
临床诊断通常需要结合影像与生理测量，但部署的模型通常处理单模态数据。我们提出PaCX-MAE，一种跨模态蒸馏框架，将生理先验注入胸部X光（CXR）编码器，同时在推理时严格保持单模态。PaCX-MAE通过双对比预测目标增强域内掩码自编码，使CXR表示与配对的ECG和实验室嵌入对齐。在九个基准上的广泛评估表明，该方法在领域特定MAE上取得一致改进，特别是在依赖生理的任务上（例如，MedMod上AUROC提升2.7；VinDr上F1提升6.5）。该方法在1%标注数据下表现出高度标签效率，并保持解剖保真度，在分割任务上与MAE持平。零样本和注意力分析证实，PaCX-MAE成功学习关注生理指标，如心脏轮廓，这在标准视觉预训练中缺失。
英文摘要：
Clinical diagnosis often requires combining imaging with physiological measurements, yet deployed models typically operate on unimodal data. We present PaCX-MAE, a cross-modal distillation framework that injects physiological priors into chest X-ray (CXR) encoders while remaining strictly unimodal at inference. PaCX-MAE augments in-domain masked autoencoding with a dual contrastive-predictive objective, aligning CXR representations with paired ECG and laboratory embeddings. Extensive evaluation across nine benchmarks demonstrates consistent improvements over domain-specific MAE, particularly on physiology-dependent tasks (e.g., +2.7 AUROC on MedMod; +6.5 F1 on VinDr). The method proves highly label-efficient in the 1% regime and preserves anatomical fidelity, achieving parity with MAE on segmentation tasks. Zero-shot and attention analyses confirm that PaCX-MAE successfully learns to attend to physiological indicators, such as the cardiac silhouette, absent in standard visual pretraining.
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