PaCX-MAE: Physiology-Augmented Chest X-Ray Masked Autoencoder

• URL: http://arxiv.org/abs/2606.01537v2
• Date: Wed, 03 Jun 2026 04:36:52 GMT
• Title: PaCX-MAE: Physiology-Augmented Chest X-Ray Masked Autoencoder
• Abstract summary: We present Pa CX-MAE, a cross-modal distillation framework that injects physiological priors into chest X-ray encoders.<n>Pa CX-MAE augments in-domain masked autoencoding with a dual contrastive-predictive objective, aligning CXR representations with paired ECG and laboratory embeddings.
• Score: 0.9767766750237933
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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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