Pathology-Aware Reconstruction with Discriminative Knowledge Boosting Alignment for Che-Xray Vision-Language Pre-training

Current medical vision-language pre-training models primarily follow two paradigms: report-supervised cross-modal alignment pre-training and reconstruction-based self-supervised pre-training. The former enhances the discriminative power of representations, while the latter facilitates fine-grained representation learning. However, naively combining these two paradigms inherits their inherent limitations: reconstruction-based methods treat all image patches equally during reconstruction, failing to effectively capture critical pathological details-since disease-related regions typically occupy only a small fraction of the image. Meanwhile, alignment-based methods suffer from suboptimal representations due to the presence of false negatives. To address these challenges, we propose a novel pre-training framework that integrates two key components: Pathology-Aware Reconstruction (PAR) and Discriminative Knowledge-Boosted Alignment (DKBA). Through a cascaded training strategy, our framework effectively combines the strengths of both paradigms while mitigating their inherent limitations. During the reconstruction pre-training stage, PAR incorporates pathology-aware priors to enhance the model's ability to capture fine-grained pathological details. In the alignment pre-training stage, DKBA leverages a medical knowledge graph as external supervision to improve cross-modal clustering alignment, thereby reducing the negative impact of false negatives. Extensive experiments on diverse downstream medical imaging tasks including image classification, object detection, and semantic segmentation, demonstrate the superior generalization capabilities of our method. Our code is publicly available at https://github.com/Felix1118/PADKB.