  Histopathology whole slide images (WSIs) can reveal significant
inter-hospital variability such as illumination, color or optical artifacts.
These variations, caused by the use of different scanning protocols across
medical centers (staining, scanner), can strongly harm algorithms
generalization on unseen protocols. This motivates development of new methods
to limit such drop of performances. In this paper, to enhance robustness on
unseen target protocols, we propose a new test-time data augmentation based on
multi domain image-to-image translation. It allows to project images from
unseen protocol into each source domain before classifying them and ensembling
the predictions. This test-time augmentation method results in a significant
boost of performances for domain generalization. To demonstrate its
effectiveness, our method has been evaluated on 2 different histopathology
tasks where it outperforms conventional domain generalization, standard H&E
specific color augmentation/normalization and standard test-time augmentation
techniques. Our code is publicly available at
https://gitlab.com/vitadx/articles/test-time-i2i-translation-ensembling.
