RadLAS: A Foundation Model for Interpretable Radiography Image Analysis with Lesion-Aware Self-Supervised Pre-training

Medical Foundation Models (MFMs) are revolutionizing radiography image analysis with scalable and generalized diagnostic capabilities. However, their effectiveness in real-world clinical practice is limited due to insufficient interpretability. To address this limitation, we propose RadLAS, a novel MFM for interpretable Radiographic image analysis by introducing Lesion-Aware Self-supervised pre-training. Unlike conventional MFMs that rely on post-hoc explanations, RadLAS innovates by directly emulating human diagnostic reasoning to first grounding lesion evidence and then making decisions accordingly. Specifically, RadLAS introduces two self-supervised tasks: (I) Lesion-grounded Reconstruction, which learns structured anatomical representations by restoring lesion-aware image patches into their healthy counterparts, thereby facilitating pixel-level grounding of lesion evidence via input-normal contrast. (II) Lesion-discrimination Contrastive Learning, which enhances lesion-aware pattern in representations by explicitly decoupling grounded lesion evidence as clinical cues and aligning them with global semantics, thereby enabling direct lesion-oriented diagnosis while preserving global context. RadLAS demonstrates excellent performance across diverse downstream radiographic datasets, offering verifiable explanations by deriving specific diagnoses (Task II) based on grounded lesion evidence (Task I), while preserving generalized representations essential for high diagnostic accuracy. Extensive experiments demonstrate that RadLAS (i) achieves superior interpretability with highly correlated lesion prediction and localization, surpassing 11 interpretable medical models; (ii) delivers scalable representation learning, outperforming 14 SOTA supervised and self-supervised MFMs.