Vascular anatomy-aware self-supervised pre-training for X-ray angiogram analysis

• URL: http://arxiv.org/abs/2602.11536v1
• Date: Thu, 12 Feb 2026 03:52:44 GMT
• Title: Vascular anatomy-aware self-supervised pre-training for X-ray angiogram analysis
• Authors: De-Xing Huang, Chaohui Yu, Xiao-Hu Zhou, Tian-Yu Xiang, Qin-Yi Zhang, Mei-Jiang Gui, Rui-Ze Ma, Chen-Yu Wang, Nu-Fang Xiao, Fan Wang, Zeng-Guang Hou,
• Abstract summary: We introduce a vascular anatomy-aware masked image modeling (VasoMIM) framework that explicitly integrates domain-specific anatomical knowledge.<n>VasoMIM comprises two key designs: an anatomy-guided masking strategy and an anatomical consistency loss.<n>In conjunction with VasoMIM, we curate XA-170K, the largest X-ray angiogram pre-training dataset to date.
• Score: 18.553513485588564
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

Related papers

• Context-Aware Asymmetric Ensembling for Interpretable Retinopathy of Prematurity Screening via Active Query and Vascular Attention [1.8420107091891775]
  Retinopathy of Prematurity (ROP) is among the major causes of preventable childhood blindness.<n>Current deep learning models depend heavily on large private datasets and passive multimodal fusion.<n>We propose the Context-Aware Asymmetric Ensemble Model (CAA Ensemble) that simulates clinical reasoning through two specialized streams.
  arXiv  Detail & Related papers  (2026-02-05T02:06:26Z)
• Multi-View Stenosis Classification Leveraging Transformer-Based Multiple-Instance Learning Using Real-World Clinical Data [76.89269238957593]
  Coronary artery stenosis is a leading cause of cardiovascular disease, diagnosed by analyzing the coronary arteries from multiple angiography views.<n>We propose SegmentMIL, a transformer-based multi-view multiple-instance learning framework for patient-level stenosis classification.
  arXiv  Detail & Related papers  (2026-02-02T13:07:52Z)
• VasoMIM: Vascular Anatomy-Aware Masked Image Modeling for Vessel Segmentation [11.157006681644136]
  We introduce Vascular anatomy-aware Masked Image Modeling (VasoMIM) for X-ray angiograms.<n>VasoMIM integrates anatomical knowledge into the pre-training process.<n>It comprises two complementary components: anatomy-guided masking strategy and anatomical consistency loss.<n>VasoMIM achieves state-of-the-art performance across three datasets.
  arXiv  Detail & Related papers  (2025-08-14T16:17:02Z)
• MLVICX: Multi-Level Variance-Covariance Exploration for Chest X-ray Self-Supervised Representation Learning [6.4136876268620115]
  MLVICX is an approach to capture rich representations in the form of embeddings from chest X-ray images. We demonstrate the performance of MLVICX in advancing self-supervised chest X-ray representation learning.
  arXiv  Detail & Related papers  (2024-03-18T06:19:37Z)
• Spatiotemporal Disentanglement of Arteriovenous Malformations in Digital Subtraction Angiography [37.44819725897024]
  The presented method aims to enhance Digital Subtraction Angiography (DSA) image series by highlighting critical information via automatic classification of vessels. The method was tested on clinical DSA images series and demonstrated efficient differentiation between arteries and veins.
  arXiv  Detail & Related papers  (2024-02-15T00:29:53Z)
• SSASS: Semi-Supervised Approach for Stenosis Segmentation [9.767759441883008]
  The complexity of coronary artery structures combined with the inherent noise in X-ray images poses a considerable challenge to this task. We introduce a semi-supervised approach for cardiovascular stenosis segmentation. Our approach demonstrated an exceptional performance in the Automatic Region-based Coronary Artery Disease diagnostics.
  arXiv  Detail & Related papers  (2023-11-17T02:01:19Z)
• MUSCLE: Multi-task Self-supervised Continual Learning to Pre-train Deep Models for X-ray Images of Multiple Body Parts [63.30352394004674]
  Multi-task Self-super-vised Continual Learning (MUSCLE) is a novel self-supervised pre-training pipeline for medical imaging tasks. MUSCLE aggregates X-rays collected from multiple body parts for representation learning, and adopts a well-designed continual learning procedure. We evaluate MUSCLE using 9 real-world X-ray datasets with various tasks, including pneumonia classification, skeletal abnormality classification, lung segmentation, and tuberculosis (TB) detection.
  arXiv  Detail & Related papers  (2023-10-03T12:19:19Z)
• Revisiting Computer-Aided Tuberculosis Diagnosis [56.80999479735375]
  Tuberculosis (TB) is a major global health threat, causing millions of deaths annually. Computer-aided tuberculosis diagnosis (CTD) using deep learning has shown promise, but progress is hindered by limited training data. We establish a large-scale dataset, namely the Tuberculosis X-ray (TBX11K) dataset, which contains 11,200 chest X-ray (CXR) images with corresponding bounding box annotations for TB areas. This dataset enables the training of sophisticated detectors for high-quality CTD.
  arXiv  Detail & Related papers  (2023-07-06T08:27:48Z)
• LVM-Med: Learning Large-Scale Self-Supervised Vision Models for Medical Imaging via Second-order Graph Matching [59.01894976615714]
  We introduce LVM-Med, the first family of deep networks trained on large-scale medical datasets. We have collected approximately 1.3 million medical images from 55 publicly available datasets. LVM-Med empirically outperforms a number of state-of-the-art supervised, self-supervised, and foundation models.
  arXiv  Detail & Related papers  (2023-06-20T22:21:34Z)
• Affinity Feature Strengthening for Accurate, Complete and Robust Vessel Segmentation [48.638327652506284]
  Vessel segmentation is crucial in many medical image applications, such as detecting coronary stenoses, retinal vessel diseases and brain aneurysms. We present a novel approach, the affinity feature strengthening network (AFN), which jointly models geometry and refines pixel-wise segmentation features using a contrast-insensitive, multiscale affinity approach.
  arXiv  Detail & Related papers  (2022-11-12T05:39:17Z)
• Many-to-One Distribution Learning and K-Nearest Neighbor Smoothing for Thoracic Disease Identification [83.6017225363714]
  deep learning has become the most powerful computer-aided diagnosis technology for improving disease identification performance. For chest X-ray imaging, annotating large-scale data requires professional domain knowledge and is time-consuming. In this paper, we propose many-to-one distribution learning (MODL) and K-nearest neighbor smoothing (KNNS) methods to improve a single model's disease identification performance.
  arXiv  Detail & Related papers  (2021-02-26T02:29:30Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.