Multistain Pretraining for Slide Representation Learning in Pathology

Understand

Developing self-supervised learning (SSL) models that can learn universal and transferable representations of H&E gigapixel whole-slide images (WSIs) is becoming increasingly valuable in computational pathology.

• These models hold the potential to advance critical tasks such as few-shot classification, slide retrieval, and patient stratification.
• Existing approaches for slide representation learning extend the principles of SSL from small images (e.g., 224 x 224 patches) to entire slides, usually by aligning two different augmentations (or views) of the slide.
• Yet the resulting representation remains constrained by the limited clinical and biological diversity of the views.