通过跨袋对比增强提升病理图像分类多样性与精度
Contrastive Cross-Bag Augmentation for Multiple Instance Learning-based Whole Slide Image Classification
- 从同类别所有切片中采样实例，扩大伪袋多样性
- 在多个数据集上超越现有方法，尤其擅长小肿瘤区域识别
- 适合病理图像分析、医学图像识别研究者使用
基于多实例学习（MIL）的全切片图像（WSI）分类中，现有伪袋增强方法仅从有限数量的袋中采样实例，导致多样性不足。为此，本文提出对比跨袋增强（C²Aug），从同类别所有袋中采样实例以提升伪袋多样性。然而，新增实例会增加关键实例（如肿瘤实例）数量，导致包含少量关键实例的伪袋减少，影响模型性能，尤其在肿瘤面积较小的测试切片上。为此，我们引入袋级与组级对比学习框架，增强具有不同语义特征的区分能力，从而提升模型表现。实验结果表明，C²Aug在多个评估指标上持续优于当前最优方法。
原文摘要 · Abstract (English)
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 ($C^2Aug$) 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. Experimental results demonstrate that $C^2Aug$ consistently outperforms state-of-the-art approaches across multiple evaluation metrics.
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