Domain adaptation using optimal transport for invariant learning using histopathology datasets
Histopathology is critical for the diagnosis of many diseases, including
cancer. These protocols typically require pathologists to manually evaluate
slides under a microscope, which is time-consuming and subjective, leading to
interest in machine learning to automate analysis. However, computational
techniques are limited by batch effects, where technical factors like
differences in preparation protocol or scanners can alter the appearance of
slides, causing models trained on one institution to fail when generalizing to
others. Here, we propose a domain adaptation method that improves the
generalization of histopathological models to data from unseen institutions,
without the need for labels or retraining in these new settings. Our approach
introduces an optimal transport (OT) loss, that extends adversarial methods
that penalize models if images from different institutions can be distinguished
in their representation space. Unlike previous methods, which operate on single
samples, our loss accounts for distributional differences between batches of
images. We show that on the Camelyon17 dataset, while both methods can adapt to
global differences in color distribution, only our OT loss can reliably
classify a cancer phenotype unseen during training. Together, our results
suggest that OT improves generalization on rare but critical phenotypes that
may only make up a small fraction of the total tiles and variation in a slide.