Self-Supervised Multiview Xray Matching
Journal:
arXiv
Published Date:
Jun 30, 2025
Abstract
Accurate interpretation of multi-view radiographs is crucial for diagnosing
fractures, muscular injuries, and other anomalies. While significant advances
have been made in AI-based analysis of single images, current methods often
struggle to establish robust correspondences between different X-ray views, an
essential capability for precise clinical evaluations. In this work, we present
a novel self-supervised pipeline that eliminates the need for manual annotation
by automatically generating a many-to-many correspondence matrix between
synthetic X-ray views. This is achieved using digitally reconstructed
radiographs (DRR), which are automatically derived from unannotated CT volumes.
Our approach incorporates a transformer-based training phase to accurately
predict correspondences across two or more X-ray views. Furthermore, we
demonstrate that learning correspondences among synthetic X-ray views can be
leveraged as a pretraining strategy to enhance automatic multi-view fracture
detection on real data. Extensive evaluations on both synthetic and real X-ray
datasets show that incorporating correspondences improves performance in
multi-view fracture classification.
