Improving Representation Learning for Histopathologic Images with Cluster Constraints

Area of Science:

• Digital Pathology
• Computational Biology
• Artificial Intelligence in Medicine

Background:

• Advances in whole-slide image (WSI) scanners and computational power enable AI in histopathology.
• Supervised learning for WSI analysis is hindered by the labor-intensive and time-consuming nature of slide labeling.

Purpose of the Study:

• To introduce a self-supervised learning (SSL) framework for whole-slide image (WSI) analysis.
• To achieve transferable representation learning and semantically meaningful clustering without explicit data annotations.

Main Methods:

• Developed an SSL framework integrating invariance loss and clustering loss for WSI analysis.
• Utilized self-supervised pretraining strategies to overcome the limitations of supervised learning.

Main Results:

• The proposed SSL framework demonstrated superior performance compared to common SSL methods.
• Achieved state-of-the-art results in downstream classification and clustering tasks on Camelyon16 and a pancreatic cancer dataset.

Conclusions:

• SSL is a viable and effective alternative to supervised learning for WSI analysis.
• The developed framework advances transferable representation learning and clustering in digital pathology.