Invisible Yet Detected: PelFANet with Attention-Guided Anatomical Fusion for Pelvic Fracture Diagnosis
This addresses diagnostic challenges for clinicians in detecting pelvic fractures, particularly in cases with subtle radiographic signs, but it is incremental as it builds on existing attention and fusion methods.
The authors tackled the problem of diagnosing pelvic fractures, especially subtle or invisible ones on X-rays, by introducing PelFANet, a dual-stream attention network that fuses raw and segmented bone images, achieving 88.68% accuracy and 0.9334 AUC for visible fractures and 82.29% accuracy and 0.8688 AUC for invisible fractures.
Pelvic fractures pose significant diagnostic challenges, particularly in cases where fracture signs are subtle or invisible on standard radiographs. To address this, we introduce PelFANet, a dual-stream attention network that fuses raw pelvic X-rays with segmented bone images to improve fracture classification. The network employs Fused Attention Blocks (FABlocks) to iteratively exchange and refine features from both inputs, capturing global context and localized anatomical detail. Trained in a two-stage pipeline with a segmentation-guided approach, PelFANet demonstrates superior performance over conventional methods. On the AMERI dataset, it achieves 88.68% accuracy and 0.9334 AUC on visible fractures, while generalizing effectively to invisible fracture cases with 82.29% accuracy and 0.8688 AUC, despite not being trained on them. These results highlight the clinical potential of anatomy-aware dual-input architectures for robust fracture detection, especially in scenarios with subtle radiographic presentations.