  Staining reveals the micro structure of the aspirate while creating
histopathology slides. Stain variation, defined as a chromatic difference
between the source and the target, is caused by varying characteristics during
staining, resulting in a distribution shift and poor performance on the target.
The goal of stain normalization is to match the target's chromatic distribution
to that of the source. However, stain normalisation causes the underlying
morphology to distort, resulting in an incorrect diagnosis. We propose FUSION,
a new method for promoting stain-adaption by adjusting the model to the target
in an unsupervised test-time scenario, eliminating the necessity for
significant labelling at the target end. FUSION works by altering the target's
batch normalization statistics and fusing them with source statistics using a
weighting factor. The algorithm reduces to one of two extremes based on the
weighting factor. Despite the lack of training or supervision, FUSION surpasses
existing equivalent algorithms for classification and dense predictions
(segmentation), as demonstrated by comprehensive experiments on two public
datasets.
