Learning representations for individual instances when only bag-level labels are available is a fundamental challenge in multiple instance learning (MIL). Recent works have shown promising results using contrastive self-supervised learning (CSSL), which learns to push apart representations corresponding to two different randomly-selected instances. Unfortunately, in real-world applications such as medical image classification, there is often class imbalance, so randomly-selected instances mostly belong to the same majority class, which precludes CSSL from learning inter-class differences. To address this issue, we propose a novel framework, Iterative Self-paced Supervised Contrastive Learning for MIL Representations (ItS2CLR), which improves the learned representation by exploiting instance-level pseudo labels derived from the bag-level labels. The framework employs a novel self-paced sampling strategy to ensure the accuracy of pseudo labels. We evaluate ItS2CLR on three medical datasets, showing that it improves the quality of instance-level pseudo labels and representations, and outperforms existing MIL methods in terms of both bag and instance level accuracy.

提出使用迭代自适应有监督对比学习来改善多示例学习表示的框架，通过使用从样本级别伪标签获得的实例级伪标签来处理类不平衡问题，并使用新颖自适应抽样策略来确保伪标签的准确性，在三个医学数据集上的实验表明，该框架提高了实例级伪标签和表示的质量，并在袋和实例级准确性方面优于现有的MIL方法。

迭代自适应自我定位对比学习的多示例学习方法