Self-Supervised Multiview Xray Matching
This addresses the challenge of precise clinical evaluations in radiology by enhancing multi-view analysis without manual annotation, though it is incremental as it builds on existing methods like transformers and synthetic data generation.
The paper tackles the problem of establishing robust correspondences between different X-ray views for clinical diagnosis by introducing a self-supervised pipeline that automatically generates many-to-many correspondences using synthetic data, and it shows that this approach improves performance in multi-view fracture classification on real datasets.
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.