用动态锚点稳定罕见病分类特征，提升胸部X光片诊断效果
Momentum-Anchored Multi-Scale Fusion Model for Long-Tailed Chest X-Ray Classification
- 引入指数移动平均作为特征锚点，防止少数类特征漂移
- 在ChestX-ray14上平均AUC达0.8682，罕见病如疝气达0.9470
- 适合医疗影像中长尾分布问题的算法改进
胸部X光片分类受严重类别不平衡影响，梯度更新偏向多数类，导致特征漂移且稀有但关键病灶分类性能差。我们提出一种动量锚定多尺度融合网络，利用指数移动平均（EMA）作为时间锚定机制，在长尾分布下稳定特征表示。方法对EfficientNet主干网络的最终扩展块实施选择性动量更新，构建缓慢演化参考分支，抵抗梯度引起的漂移，同时保留少数类的判别特征。结合多尺度空间融合（1×1、3×3、5×5卷积），该策略在整个训练过程中保持表征稳定性。在ChestX-ray14数据集上，本方法平均AUC达到0.8682，优于现有先进方法，在罕见病如疝气（0.9470）和肺炎（0.8165）上表现尤为突出。结果表明，动量锚定能有效缓解长尾医学图像分类中的特征不稳定性。
原文摘要 · Abstract (English)
Chest X-ray classification suffers from severe class imbalance where gradient updates bias toward majority classes, causing feature drift and poor performance on rare but critical pathologies. We propose a Momentum-Anchored Multi-Scale Fusion Network that uses exponential moving averages (EMA) as a temporal anchoring mechanism to stabilize feature representations under long-tailed distributions. Our approach applies selective momentum updates to the final expansion block of an EfficientNet backbone, creating a slowly-evolving reference branch that resists gradient-induced drift while preserving discriminative patterns for minority classes. Combined with multi-scale spatial fusion ($1\times 1$, $3 \times 3$, $5 \times 5$ convolutions), this anchoring strategy maintains representational stability throughout training. On ChestX-ray14, our method achieves 0.8682 average AUC, outperforming state-of-the-art approaches and showing particular improvements on rare pathologies like Hernia (0.9470) and Pneumonia (0.8165). The results demonstrate that momentum anchoring effectively counters feature instability in long-tailed medical image classification.
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