用方差风险外推法提升多医院肺部CT判新冠的泛化能力。
Multi-Source COVID-19 Detection via Variance Risk Extrapolation
- 引入方差风险外推，降低不同医院数据间的性能波动。
- 在四个医院数据上平均宏F1达0.96，表现稳定可靠。
- 适合需要跨机构部署的医学影像诊断系统使用。
我们针对多源新冠肺炎检测挑战赛提出解决方案，旨在对来自四家不同医院和医疗中心的胸部CT扫描进行新冠与非新冠分类。该任务的主要挑战在于各机构间成像协议、扫描设备及患者群体差异导致的领域偏移。为提升模型跨域泛化能力，我们在训练中引入方差风险外推（VREx），通过显式最小化多个源域上的经验风险方差，促使模型在不同环境中保持一致性能。该正则化策略抑制了对特定中心特征的过拟合，促进学习领域不变表示。此外，我们采用Mixup数据增强，通过对随机选取的样本对输入和标签进行插值，使模型在样本间表现出线性行为，增强对噪声和小样本的鲁棒性。我们的方法在验证集上于四个来源数据上实现了平均宏F1分数0.96，展现出优异的泛化能力。
原文摘要 · Abstract (English)
We present our solution for the Multi-Source COVID-19 Detection Challenge, which aims to classify chest CT scans into COVID and Non-COVID categories across data collected from four distinct hospitals and medical centers. A major challenge in this task lies in the domain shift caused by variations in imaging protocols, scanners, and patient populations across institutions. To enhance the cross-domain generalization of our model, we incorporate Variance Risk Extrapolation (VREx) into the training process. VREx encourages the model to maintain consistent performance across multiple source domains by explicitly minimizing the variance of empirical risks across environments. This regularization strategy reduces overfitting to center-specific features and promotes learning of domain-invariant representations. We further apply Mixup data augmentation to improve generalization and robustness. Mixup interpolates both the inputs and labels of randomly selected pairs of training samples, encouraging the model to behave linearly between examples and enhancing its resilience to noise and limited data. Our method achieves an average macro F1 score of 0.96 across the four sources on the validation set, demonstrating strong generalization.
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