  Pre-processing for whole slide images can affect classification performance
both in the training and inference stages. Our study analyzes the impact of
pre-processing parameters on inference and training across single- and
multiple-domain datasets. However, searching for an optimal parameter set is
time-consuming. To overcome this, we propose a novel Similarity-based Simulated
Annealing approach for fast parameter tuning to enhance inference performance
on single-domain data. Our method demonstrates significant performance
improvements in accuracy, which raise accuracy from 0.512 to 0.847 in a single
domain. We further extend our insight into training performance in multi-domain
data by employing a novel Bayesian optimization to search optimal
pre-processing parameters, resulting in a high AUC of 0.967. We highlight that
better pre-processing for WSI can contribute to further accuracy improvement in
the histology area.
