  Performance of deep learning algorithms decreases drastically if the data
distributions of the training and testing sets are different. Due to variations
in staining protocols, reagent brands, and habits of technicians, color
variation in digital histopathology images is quite common. Color variation
causes problems for the deployment of deep learning-based solutions for
automatic diagnosis system in histopathology. Previously proposed color
normalization methods consider a small patch as a reference for normalization,
which creates artifacts on out-of-distribution source images. These methods are
also slow as most of the computation is performed on CPUs instead of the GPUs.
We propose a color normalization technique, which is fast during its
self-supervised training as well as inference. Our method is based on a
lightweight fully-convolutional neural network and can be easily attached to a
deep learning-based pipeline as a pre-processing block. For classification and
segmentation tasks on CAMELYON17 and MoNuSeg datasets respectively, the
proposed method is faster and gives a greater increase in accuracy than the
state of the art methods.
