Improving Representation Learning for Histopathologic Images with Cluster Constraints
This work addresses the problem of reducing annotation burden for histopathologic image analysis, representing an incremental improvement in self-supervised learning methods for medical imaging.
The paper tackles the challenge of labor-intensive labeling in whole-slide image analysis by introducing a self-supervised learning framework that combines invariance and clustering losses, outperforming common SSL methods in classification and clustering tasks on datasets like Camelyon16 and a pancreatic cancer dataset.
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.