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Staining reveals the micro structure of the aspirate while creatinghistopathology slides. Stain variation, defined as a chromatic differencebetween the source and the target, is caused by varying characteristics duringstaining, resulting in a distribution shift and poor performance on the target.The goal of stain normalization is to match the target's chromatic distributionto that of the source. However, stain normalisation causes the underlyingmorphology to distort, resulting in an incorrect diagnosis. We propose FUSION,a new method for promoting stain-adaption by adjusting the model to the targetin an unsupervised test-time scenario, eliminating the necessity forsignificant labelling at the target end. FUSION works by altering the target'sbatch normalization statistics and fusing them with source statistics using aweighting factor. The algorithm reduces to one of two extremes based on theweighting factor. Despite the lack of training or supervision, FUSION surpassesexisting equivalent algorithms for classification and dense predictions(segmentation), as demonstrated by comprehensive experiments on two publicdatasets.

FUSION: Fully Unsupervised Test-Time Stain Adaptation via Fused Normalization Statistics