通过混合不同数据集的样本，提升乳腺MRI肿瘤分类模型跨机构泛化能力。
Cross-Dataset Generalization in Breast MRI Tumor Classification via Class-Wise Dataset Mixing
- 按类别混合来自不同数据集的样本，控制患者级划分与泄漏风险。
- 在多中心测试集上，F1最高达0.8994，显著优于仅用单一数据集训练的模型。
- 适合关注医学影像跨机构泛化、避免数据源偏差的研究者。
乳腺MRI对检测乳腺肿瘤高度敏感，但检查包含大量切片，需耗费大量阅片时间。深度学习模型在内部划分上表现良好，但在跨机构场景中常因域偏移和数据集来源偏差而失效。本文研究二分类乳腺MRI肿瘤分类中的这一失败模式。使用Duke乳腺癌MRI和fastMRI数据集训练EfficientNet-B3和WaveViT-Small模型，并仅在独立的多中心MAMA-MIA队列上评估。在人为混淆的设定下，标签与数据集来源完全相关时，外部准确率接近随机水平（0.5048–0.5265），尽管召回率极高。随后构建混合训练集，每个类别均包含来自Duke和fastMRI的样本，同时保持患者级划分、增强和泄漏控制。在MAMA-MIA上，数据集混合使WaveViT-Small的准确率/F1提升至0.8463/0.8625，EfficientNet-B3达到0.8884/0.8994。结果表明，控制数据集来源偏差对可靠乳腺MRI分类至关重要。
原文摘要 · Abstract (English)
Breast MRI is highly sensitive for detecting breast tumors, but exams contain many slices and require substantial reading time. Deep learning models often perform well on internal splits but can fail across institutions because of domain shift and dataset-origin bias. We study this failure mode for binary breast MRI tumor classification. EfficientNet-B3 and WaveViT-Small are trained using Duke Breast Cancer MRI and fastMRI, and evaluated only on the independent multi-center MAMA-MIA cohort. In a deliberately confounded setup, where label is perfectly correlated with dataset origin, external accuracy is near chance (0.5048--0.5265), despite very high recall. We then construct a mixed training set in which each class contains samples from both Duke and fastMRI, while preserving patient-level splitting, augmentation, and leakage controls. On MAMA-MIA, dataset mixing improves accuracy/F1 to 0.8463/0.8625 for WaveViT-Small and 0.8884/0.8994 for EfficientNet-B3. These results show that controlling dataset-origin bias is important for reliable breast MRI classification.
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