Abstract
Weakly supervised multiple instance learning (MIL) is a challenging task given that only bag-level labels are provided, while each bag typically contains multiple instances. This topic has been extensively studied in histopathological image analysis, where labels are usually available only at the whole slide image (WSI) level, while each whole slide image can be divided into thousands of small image patches for training. The dominant MIL approaches take fixed patch features as inputs to address computational constraints and ensure model stability. These features are commonly generated by encoders pre-trained on ImageNet, foundation encoders pre-trained on large datasets, or through self-supervised learning on local datasets. While the self-supervised encoder pre-training on the same dataset as downstream MIL tasks helps mitigate domain shift and generate better features, the bag-level labels are not utilized during the process, and the features of patches from different categories may cluster together, reducing classification performance on MIL tasks. Recently, pre-training with supervised contrastive learning (SupCon) has demonstrated superior performance compared to self-supervised contrastive learning and even end-to-end training on traditional image classification tasks. In this paper, we propose a novel encoder pre-training method for downstream MIL tasks called Weakly Supervised Contrastive Learning (WeakSupCon) that utilizes bag-level labels. In our method, we employ multi-task learning and define distinct contrastive learning losses for samples with different bag labels. Our experiments demonstrate that the features generated using WeakSupCon significantly enhance MIL classification performance compared to self-supervised approaches across three datasets.
Abstract (translated)
弱监督多实例学习（MIL）是一个具有挑战性的任务，因为仅提供了袋子级别的标签，而每个袋子通常包含多个实例。这一主题在组织病理图像分析领域得到了广泛研究，在该领域中，标签通常是针对整个切片图像（WSI）级别提供的，然而每个完整的切片可以被分割成数千个小图像块用于训练。目前主导的MIL方法使用固定的补丁特征作为输入以解决计算限制并确保模型稳定性。这些特征通常通过在ImageNet上预训练的编码器、大型数据集上的基础编码器或本地数据集上的自监督学习生成。 尽管在相同的数据集上进行自我监督编码器预训练有助于缓解领域转移，并生成更好的特征，但袋子级别的标签并未在此过程中被利用；来自不同类别的补丁可能聚集在一起，从而降低了MIL任务中的分类性能。最近，使用监督对比学习（SupCon）的预训练方法在传统图像分类任务上展示出优于自监督对比学习甚至端到端训练的表现。在本文中，我们提出了一种针对下游MIL任务的新颖编码器预训练方法——弱监督对比学习（WeakSupCon），这种方法利用了袋子级别的标签。我们的方法采用了多任务学习，并为具有不同袋子标签的样本定义了不同的对比学习损失。 实验结果表明，在三个数据集上使用WeakSupCon生成的特征显著提升了MIL分类性能，优于自监督方法。
URL
https://arxiv.org/abs/2503.04165
