用卷积网络提升骨骼密度预测准确率，更适配临床部署
MedConv: Convolutions Beat Transformers on Long-Tailed Bone Density Prediction
- 采用卷积结构替代Transformer，降低计算开销
- 在真实数据上实现21%准确率提升，ROC AUC提高20%
- 适合关注医疗影像模型轻量化与长尾分布问题的研究者
通过CT扫描预测骨密度以估算T值至关重要，相比传统X射线方法具有更高的空间分辨率和局部变化检测能力。然而，基于CT的预测面临两大挑战：基于Transformer的架构计算复杂度高，限制了其在便携式和临床场景中的部署；真实医院数据存在严重不平衡的长尾分布，导致预测偏差。为此，我们提出MedConv，一种用于骨密度预测的卷积模型，其性能优于现有Transformer模型且计算需求更低。同时，我们引入Bal-CE损失和后处理对数调整以改善类别平衡。在我们的AustinSpine数据集上的大量实验表明，该方法相较先前最先进方法，在准确率上最高提升21%，在ROC AUC上提升20%。
原文摘要 · Abstract (English)
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.
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