  Recent advances in whole-slide image (WSI) scanners and computational
capabilities have significantly propelled the application of artificial
intelligence in histopathology slide analysis. While these strides are
promising, current supervised learning approaches for WSI analysis come with
the challenge of exhaustively labeling high-resolution slides - a process that
is both labor-intensive and time-consuming. In contrast, self-supervised
learning (SSL) pretraining strategies are emerging as a viable alternative,
given that they don't rely on explicit data annotations. These SSL strategies
are quickly bridging the performance disparity with their supervised
counterparts. In this context, we introduce an SSL framework. This framework
aims for transferable representation learning and semantically meaningful
clustering by synergizing invariance loss and clustering loss in WSI analysis.
Notably, our approach outperforms common SSL methods in downstream
classification and clustering tasks, as evidenced by tests on the Camelyon16
and a pancreatic cancer dataset.
