Leveraging Gait Patterns as Biomarkers: An attention-guided Deep Multiple Instance Learning Network for Scoliosis Classification
Journal:
arXiv
Published Date:
Apr 4, 2025
Abstract
Scoliosis is a spinal curvature disorder that is difficult to detect early
and can compress the chest cavity, impacting respiratory function and cardiac
health. Especially for adolescents, delayed detection and treatment result in
worsening compression. Traditional scoliosis detection methods heavily rely on
clinical expertise, and X-ray imaging poses radiation risks, limiting
large-scale early screening. We propose an Attention-Guided Deep Multi-Instance
Learning method (Gait-MIL) to effectively capture discriminative features from
gait patterns, which is inspired by ScoNet-MT's pioneering use of gait patterns
for scoliosis detection. We evaluate our method on the first large-scale
dataset based on gait patterns for scoliosis classification. The results
demonstrate that our study improves the performance of using gait as a
biomarker for scoliosis detection, significantly enhances detection accuracy
for the particularly challenging Neutral cases, where subtle indicators are
often overlooked. Our Gait-MIL also performs robustly in imbalanced scenarios,
making it a promising tool for large-scale scoliosis screening.
