  Stain variation is a phenomenon observed when distinct pathology laboratories
stain tissue slides that exhibit similar but not identical color appearance.
Due to this color shift between laboratories, convolutional neural networks
(CNNs) trained with images from one lab often underperform on unseen images
from the other lab. Several techniques have been proposed to reduce the
generalization error, mainly grouped into two categories: stain color
augmentation and stain color normalization. The former simulates a wide variety
of realistic stain variations during training, producing stain-invariant CNNs.
The latter aims to match training and test color distributions in order to
reduce stain variation. For the first time, we compared some of these
techniques and quantified their effect on CNN classification performance using
a heterogeneous dataset of hematoxylin and eosin histopathology images from 4
organs and 9 pathology laboratories. Additionally, we propose a novel
unsupervised method to perform stain color normalization using a neural
network. Based on our experimental results, we provide practical guidelines on
how to use stain color augmentation and stain color normalization in future
computational pathology applications.
