面向多实例学习全切片图像分类的对比跨袋增强
Contrastive Cross-Bag Augmentation for Multiple Instance Learning-based Whole Slide Image Classification
C2Aug 的跨袋增强与袋级/组级对比学习框架可迁移到其他弱监督密集预测任务（如组织病理分割、遥感场景分类），用于提升伪包多样性和特征判别性。
原文依据 we propose Contrastive Cross-Bag Augmentation (C2Aug) to sample instances from all bags with the same class to increase the diversity of pseudo-bags.
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论近期基于多实例学习（MIL）的全切片图像（WSI）分类中的伪袋增强方法从有限数量的袋中采样实例，导致多样性受限。为解决该问题，我们提出对比跨袋增强（C2Aug），从所有具有相同类别的袋中采样实例以增加伪袋的多样性。然而，将新实例引入伪袋会增加关键实例（例如肿瘤实例）的数量。这种增加导致包含少量关键实例的伪袋出现频率降低，从而限制模型性能，尤其是在肿瘤区域较小的测试切片上。为解决该问题，我们引入袋级和组级对比学习框架，以增强具有不同语义含义的特征的判别性，从而提高模型性能。C2Aug 在整个数据集上采样具有一致袋级标签的实例，这同时增强了伪袋的多样性并减轻了标签噪声。实验结果表明，C2Aug 在多个评估指标上始终优于最优方法。我们的代码公开于：https://github.com/weiaicunzai/mixup。
英文原文摘要 1261 字符
Recent pseudo-bag augmentation methods for Multiple Instance Learning (MIL)-based Whole Slide Image (WSI) classification sample instances from a limited number of bags, resulting in constrained diversity. To address this issue, we propose Contrastive Cross-Bag Augmentation (C2Aug) to sample instances from all bags with the same class to increase the diversity of pseudo-bags. However, introducing new instances into the pseudo-bag increases the number of critical instances (e.g., tumor instances). This increase results in a reduced occurrence of pseudo-bags containing few critical instances, thereby limiting model performance, particularly on test slides with small tumor areas. To address this, we introduce a bag-level and group-level contrastive learning framework to enhance the discrimination of features with distinct semantic meanings, thereby improving model performance. C2Aug samples instances with consistent bag-level labels across the entire dataset, which simultaneously enhances the diversity of pseudo-bags and mitigates label noise. Experimental results demonstrate that C2Aug consistently outperforms state-of-the-art approaches across multiple evaluation metrics. Our code is publicly available at: https://github.com/weiaicunzai/mixup.