A self-supervised framework for learning whole slide representations
Xinhai Hou Cheng Jiang Akhil Kondepudi Yiwei Lyu Asadur Zaman Chowdury Honglak Lee Todd C. Hollon
Whole slide imaging is fundamental to biomedical microscopy and computational
pathology. However, whole slide images (WSIs) present a complex computer vision
challenge due to their gigapixel size, diverse histopathologic features,
spatial heterogeneity, and limited/absent data annotations. These challenges
highlight that supervised training alone can result in suboptimal whole slide
representations. Self-supervised representation learning can achieve
high-quality WSI visual feature learning for downstream diagnostic tasks, such
as cancer diagnosis or molecular genetic prediction. Here, we present a general
self-supervised whole slide learning (S3L) framework for gigapixel-scale
self-supervision of WSIs. S3L combines data transformation strategies from
transformer-based vision and language modeling into a single unified framework
to generate paired views for self-supervision. S3L leverages the inherent
regional heterogeneity, histologic feature variability, and information
redundancy within WSIs to learn high-quality whole-slide representations. We
benchmark S3L visual representations on two diagnostic tasks for two biomedical
microscopy modalities. S3L significantly outperforms WSI baselines for cancer
diagnosis and genetic mutation prediction. Additionally, S3L achieves good
performance using both in-domain and out-of-distribution patch encoders,
demonstrating good flexibility and generalizability.