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.

经过预处理，全幻灯片图像对分类性能有影响，我们的研究通过分析单领域和多领域数据集上预处理参数对推断和训练的影响，并提出了一种新的基于相似性的模拟退火方法来快速调整参数以提高单领域数据的推断性能，从而在该领域的准确性从 0.512 提高到 0.847。此外，我们还利用一种新的贝叶斯优化方法来搜索最佳预处理参数以提高多领域数据的训练性能，结果得到了 0.967 的高 AUC 值。我们强调 WSI 的更好预处理可以进一步提高组织学领域的准确性。