  Local representation learning has been a key challenge to promote the
performance of the histopathological whole slide images analysis. The previous
representation learning methods followed the supervised learning paradigm.
However, manual annotation for large-scale WSIs is time-consuming and
labor-intensive. Hence, the self-supervised contrastive learning has recently
attracted intensive attention. The present contrastive learning methods treat
each sample as a single class, which suffers from class collision problems,
especially in the domain of histopathology image analysis. In this paper, we
proposed a novel contrastive representation learning framework named
Lesion-Aware Contrastive Learning (LACL) for histopathology whole slide image
analysis. We built a lesion queue based on the memory bank structure to store
the representations of different classes of WSIs, which allowed the contrastive
model to selectively define the negative pairs during the training. Moreover,
We designed a queue refinement strategy to purify the representations stored in
the lesion queue. The experimental results demonstrate that LACL achieves the
best performance in histopathology image representation learning on different
datasets, and outperforms state-of-the-art methods under different WSI
classification benchmarks. The code is available at
https://github.com/junl21/lacl.
