Efficient and Gender-Adaptive Graph Vision Mamba for Pediatric Bone Age Assessment

Lingyu Zhou, Zhang Yi, Kai Zhou, Xiuyuan Xu

Abstract: Bone age assessment (BAA) is crucial for evaluating the skeletal maturity of children in pediatric clinics. The decline in assessment accuracy is attributed to the existence of inter-gender disparity. Current automatic methods bridge this gap by relying on bone regions of interest and gender, resulting in high annotation costs. Meanwhile, the models still grapple with efficiency bottleneck for lightweight deployment. To address these challenges, this study presents Gender-adaptive Graph Vision Mamba (GGVMamba) framework with only raw X-ray images. Concretely, a region augmentation process, called directed scan module, is proposed to integrate local context from various directions of bone X-ray images. Then we construct a novel graph Mamba encoder with linear complexity, fostering robust modelling for both within and among region features. Moreover, a gender adaptive strategy is proposed to improve gender consistency by dynamically selecting gender-specific graph structures. Experiments demonstrate that GGVMamba obtains state-of-the-art results with MAE of 3.82, 4.91, and 4.14 on RSNA, RHPE, and DHA, respectively. Notably, GGVMamba shows exceptional gender consistency and optimal efficiency with minimal GPU load. The code is available at https://github.com/SCU-zly/GGVMamba.