Pre-processing matters: A segment search method for WSI classification
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.
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