在数据稀缺的医疗影像分类中，用小开销实现公平且稳定的模型集成。
OxEnsemble: Fair Ensembles for Low-Data Classification
- 通过多模型集成并强制每成员满足公平性约束，提升低数据场景下的公平性
- 在多个医学影像数据集上，公平性与准确率平衡优于现有方法
- 仅需微调或推理计算量，适合资源受限的医疗场景
我们针对数据稀缺且群体间不平衡的公平分类问题提出新方法 OxEnsemble。该场景常见于医疗影像领域，误诊可能导致致命后果。OxEnsemble 通过聚合多个满足公平性约束的模型预测，实现高效训练与公平保障。其设计兼具数据效率——通过合理复用验证数据强化公平性约束——和计算效率，额外开销仅略高于微调或评估单个模型。我们提供了新的理论保证。实验表明，该方法在多个具有挑战性的医学影像分类数据集上，相较现有方法展现出更一致的结果和更强的公平性-准确率权衡能力。
原文摘要 · Abstract (English)
We address the problem of fair classification in settings where data is scarce and unbalanced across demographic groups. Such low-data regimes are common in domains like medical imaging, where false negatives can have fatal consequences. We propose a novel approach \emph{OxEnsemble} for efficiently training ensembles and enforcing fairness in these low-data regimes. Unlike other approaches, we aggregate predictions across ensemble members, each trained to satisfy fairness constraints. By construction, \emph{OxEnsemble} is both data-efficient -- carefully reusing held-out data to enforce fairness reliably -- and compute-efficient, requiring little more compute than used to fine-tune or evaluate an existing model. We validate this approach with new theoretical guarantees. Experimentally, our approach yields more consistent outcomes and stronger fairness-accuracy trade-offs than existing methods across multiple challenging medical imaging classification datasets.
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