  Recent advancements in digital pathology have led to the development of
numerous foundational models that utilize self-supervised learning on patches
extracted from gigapixel whole slide images (WSIs). While this approach
leverages vast amounts of unlabeled data, we have discovered a significant
issue: features extracted from these self-supervised models tend to cluster by
individual WSIs, a phenomenon we term WSI-specific feature collapse. This
problem can potentially limit the model's generalization ability and
performance on various downstream tasks. To address this issue, we introduce
Stain Normalized Pathology Foundational Model, a novel foundational model
trained on patches that have undergone stain normalization. Stain normalization
helps reduce color variability arising from different laboratories and
scanners, enabling the model to learn more consistent features. Stain
Normalized Pathology Foundational Model is trained using 285,153,903 patches
extracted from a total of 34,795 WSIs, combining data from The Cancer Genome
Atlas (TCGA) and the Genotype-Tissue Expression (GTEx) project. Our experiments
demonstrate that Stain Normalized Pathology Foundational Model significantly
mitigates the feature collapse problem, indicating that the model has learned
more generalized features rather than overfitting to individual WSI
characteristics. We compared Stain Normalized Pathology Foundational Model with
state-of-the-art models across six downstream task datasets, and our results
show that Stain Normalized Pathology Foundational Model achieves excellent
performance relative to the number of WSIs used and the model's parameter
count. This suggests that the application of stain normalization has
substantially improved the model's efficiency and generalization capabilities.
