A Two‐Stage Multi‐View Fusion Framework With Semantic‐Spatial Alignment for Precise Diagnosis of Phalangeal Fractures

Abstract

ABSTRACT Hand fractures, particularly phalangeal fractures, are often subtle, concealed, and complex, posing significant challenges for accurate and efficient diagnosis with traditional radiography. To address the limitations of existing deep learning methods in handling complex anatomy, multi‐view uncertainty, and causal discrimination, we propose a novel two‐stage context‐aware multi‐view fusion framework for precise fracture classification and localization. In the first stage, a Context‐Aware Multi‐View Classification Network (CAMVC‐Net) is developed. A Dual‐path Semantic‐Spatial Alignment (DSSA) module aligns low‐level spatial details with high‐level semantics, enhancing fine‐grained fracture discrimination. A Context Aware Pyramid (CAP) module captures multi‐scale context, while a Counterfactual Attention Learning (CAL) loss guides the network to focus on discriminative regions. Multi‐view uncertainty is modeled using a Dirichlet distribution, and decision‐level fusion is achieved by Dempster's rule to mimic clinical reasoning. In the second stage, a Detail‐aware Fracture Localization Network (DFL‐Net) is designed. To adapt to irregular fracture geometry, DFL‐Net integrates deformable convolutions and incorporates DSSA into the feature pyramid to preserve fine spatial details during deep downsampling. Experiments on the MURA and clinical datasets demonstrate strong performance: the classification model achieved an accuracy of 92.89%, precision of 93.05%, recall of 96.96%, and F1 score of 94.97%. The localization network obtained 73.8% AP 50 and 43.8% AP 75 , with a 10.4% improvement in AP 75 over Faster R‐CNN. These results indicate that the proposed framework provides an accurate and efficient tool for computer‐aided fracture diagnosis, with the potential to reduce misdiagnosis and improve clinical decision‐making efficiency.

Ähnliche Arbeiten