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The paper introduces VasoMIM, a vascular anatomy-aware masked image modeling framework for self-supervised learning on X-ray angiograms, addressing the scarcity of annotated data in this domain. VasoMIM uses an anatomy-guided masking strategy and an anatomical consistency loss to improve the learning of vascular semantics and structural consistency. The framework is pre-trained on XA-170K, a newly curated large-scale X-ray angiogram dataset, and achieves state-of-the-art performance on four downstream tasks across six datasets, demonstrating its transferability.
By incorporating anatomical priors into masked image modeling, VasoMIM significantly boosts performance on X-ray angiogram analysis tasks, outperforming existing self-supervised methods.
X-ray angiography is the gold standard imaging modality for cardiovascular diseases. However, current deep learning approaches for X-ray angiogram analysis are severely constrained by the scarcity of annotated data. While large-scale self-supervised learning (SSL) has emerged as a promising solution, its potential in this domain remains largely unexplored, primarily due to the lack of effective SSL frameworks and large-scale datasets. To bridge this gap, we introduce a vascular anatomy-aware masked image modeling (VasoMIM) framework that explicitly integrates domain-specific anatomical knowledge. Specifically, VasoMIM comprises two key designs: an anatomy-guided masking strategy and an anatomical consistency loss. The former strategically masks vessel-containing patches to compel the model to learn robust vascular semantics, while the latter preserves structural consistency of vessels between original and reconstructed images, enhancing the discriminability of the learned representations. In conjunction with VasoMIM, we curate XA-170K, the largest X-ray angiogram pre-training dataset to date. We validate VasoMIM on four downstream tasks across six datasets, where it demonstrates superior transferability and achieves state-of-the-art performance compared to existing methods. These findings highlight the significant potential of VasoMIM as a foundation model for advancing a wide range of X-ray angiogram analysis tasks. VasoMIM and XA-170K will be available at https://github.com/Dxhuang-CASIA/XA-SSL.