  Differences in staining and imaging procedures can cause significant color
variations in histopathology images, leading to poor generalization when
deploying deep-learning models trained from a different data source. Various
color augmentation methods have been proposed to generate synthetic images
during training to make models more robust, eliminating the need for stain
normalization during test time. Many color augmentation methods leverage domain
labels to generate synthetic images. This approach causes three significant
challenges to scaling such a model. Firstly, incorporating data from a new
domain into deep-learning models trained on existing domain labels is not
straightforward. Secondly, dependency on domain labels prevents the use of
pathology images without domain labels to improve model performance. Finally,
implementation of these methods becomes complicated when multiple domain labels
(e.g., patient identification, medical center, etc) are associated with a
single image. We introduce ContriMix, a novel domain label free stain color
augmentation method based on DRIT++, a style-transfer method. Contrimix
leverages sample stain color variation within a training minibatch and random
mixing to extract content and attribute information from pathology images. This
information can be used by a trained ContriMix model to create synthetic images
to improve the performance of existing classifiers. ContriMix outperforms
competing methods on the Camelyon17-WILDS dataset. Its performance is
consistent across different slides in the test set while being robust to the
color variation from rare substances in pathology images. We make our code and
trained ContriMix models available for research use. The code for ContriMix can
be found at https://gitlab.com/huutan86/contrimix
