用步态视频和注意力模型实现高效、低成本的青少年脊柱侧弯筛查
TokenSTFormer: A Tokenized Spatial-temporal Attention Model for Holistic Motion Analysis in Adolescent Idiopathic Scoliosis Screening
- 将时空特征分块为令牌，提升动作分析的表征能力
- 在1516段步态视频上达到79%准确率，优于传统视觉变换器
- 适合医疗筛查场景，可大规模推广用于脊柱侧弯早期发现
青少年特发性脊柱侧弯（AIS）是青少年常见脊柱畸形，若不及时干预可能引发严重健康问题。传统筛查方法受限于主观判断、依赖专业经验且难以规模化。为此，我们构建了ScoliGait数据集，包含1,516段步态视频及对应X光片。提出TokenSTFormer模型，通过分词化处理空间与时间语义，增强特征表示并加速收敛。该模型在关键指标上超越基础视觉变换器，准确率达0.79。研究表明，利用步态视频中的整体运动特征与基于注意力的模型，可实现可扩展、低成本的AIS筛查，为未来临床应用提供新路径。
原文摘要 · Abstract (English)
Adolescent Idiopathic Scoliosis (AIS) is a prevalent spinal deformity in adolescents that, if left untreated, can result in severe health outcomes. Traditional screening methods are limited by subjective interpretation, reliance on professional expertise and low scalability. To address these challenges, we present ScoliGait dataset, which comprises 1,516 gait video clips paired with corresponding X-ray records. We also introduce TokenSTFormer, a novel model that tokenizes spatial and temporal semantics to enhance feature representation and convergence. Our model achieves state-of-the-art performance, surpassing vanilla Vision Transformer encoder across key metrics, including accuracy of 0.79. This study highlights the potential of leveraging holistic motion features derived from gait video and attention-based models for scalable, cost-effective AIS screening, paving the way for future clinical applications in scoliosis detection.
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