  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. Code is available at
https://github.com/Kangningthu/ItS2CLR
