标题： 基于方差风险外推的多源新冠病毒检测 显示英文标题

标题： Multi-Source COVID-19 Detection via Variance Risk Extrapolation

摘要： 我们提出了针对多源冠状病毒检测挑战的解决方案，该挑战旨在将来自四个不同医院和医疗中心的数据收集的胸部CT扫描分类为冠状病毒和非冠状病毒类别。 此任务中的主要挑战在于由于机构间成像协议、扫描仪和患者群体的差异引起的领域偏移。 为了增强模型的跨领域泛化能力，我们在训练过程中引入了方差风险外推（VREx）。 VREx通过显式最小化不同环境中的经验风险方差，促使模型在多个源领域中保持一致的性能。 这种正则化策略减少了对中心特定特征的过拟合，并促进了领域不变表示的学习。 我们进一步应用Mixup数据增强来提高泛化能力和鲁棒性。 Mixup对随机选择的训练样本对的输入和标签进行插值，鼓励模型在示例之间表现出线性行为，并增强其对噪声和有限数据的抵抗力。 我们的方法在验证集上的四个来源上平均宏F1得分为0.96，证明了强大的泛化能力。 显示英文摘要

摘要： 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.