Abstract
Digital histopathology whole slide images (WSIs) provide gigapixel-scale high-resolution images that are highly useful for disease diagnosis. However, digital histopathology image analysis faces significant challenges due to the limited training labels, since manually annotating specific regions or small patches cropped from large WSIs requires substantial time and effort. Weakly supervised multiple instance learning (MIL) offers a practical and efficient solution by requiring only bag-level (slide-level) labels, while each bag typically contains multiple instances (patches). Most MIL methods directly use frozen image patch features generated by various image encoders as inputs and primarily focus on feature aggregation. However, feature representation learning for encoder pretraining in MIL settings has largely been neglected. In our work, we propose a novel feature representation learning framework called weakly supervised contrastive learning (WeakSupCon) that incorporates bag-level label information during training. Our method does not rely on instance-level pseudo-labeling, yet it effectively separates patches with different labels in the feature space. Experimental results demonstrate that the image features generated by our WeakSupCon method lead to improved downstream MIL performance compared to self-supervised contrastive learning approaches in three datasets. Our related code is available at this http URL
Abstract (translated)
数字全滑病理图像（WSIs）提供了像素级的高分辨率图像，这对于疾病诊断非常有用。然而，由于标注训练数据量有限，数字病理图像分析面临着重大挑战，因为手动注释特定区域或从大型WSI中裁剪的小块需要大量的时间和精力。弱监督下的多实例学习（MIL）通过只需要袋子级别的标签提供了一种实用且高效的解决方案，而每个袋子通常包含多个实例（斑块）。大多数MIL方法直接使用由各种图像编码器生成的冻结图像斑块特征作为输入，并主要集中在特征聚合上。然而，在MIL环境中用于编码器预训练的特征表示学习被很大程度忽视了。 在我们的工作中，我们提出了一种名为弱监督对比学习（WeakSupCon）的新颖特征表示学习框架，该框架在训练过程中结合使用袋子级别的标签信息。我们的方法不依赖于实例级别伪标记，但在特征空间中有效地将具有不同标签的斑块分开。实验结果表明，在三个数据集中，我们提出的WeakSupCon方法生成的图像特征相比于自监督对比学习方法带来了更好的下游MIL性能改进。 相关代码可在此链接访问：[此URL]（请根据实际发布的地址替换）。
URL
https://arxiv.org/abs/2602.09477
