MedConv: Convolutions Beat Transformers on Long-Tailed Bone Density Prediction
Journal:
arXiv
Published Date:
Feb 2, 2025
Abstract
Bone density prediction via CT scans to estimate T-scores is crucial,
providing a more precise assessment of bone health compared to traditional
methods like X-ray bone density tests, which lack spatial resolution and the
ability to detect localized changes. However, CT-based prediction faces two
major challenges: the high computational complexity of transformer-based
architectures, which limits their deployment in portable and clinical settings,
and the imbalanced, long-tailed distribution of real-world hospital data that
skews predictions. To address these issues, we introduce MedConv, a
convolutional model for bone density prediction that outperforms transformer
models with lower computational demands. We also adapt Bal-CE loss and post-hoc
logit adjustment to improve class balance. Extensive experiments on our
AustinSpine dataset shows that our approach achieves up to 21% improvement in
accuracy and 20% in ROC AUC over previous state-of-the-art methods.
