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arXiv · 2404.11161

BAHOP: Similarity-based Basin Hopping for A fast hyper-parameter search in WSI classification

Jun Wang·Yu Mao·Yufei Cui·Nan Guan·Chun Jason Xue
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Abstract

Pre-processing whole slide images (WSIs) can impact classification performance. Our study shows that using fixed hyper-parameters for pre-processing out-of-domain WSIs can significantly degrade performance. Therefore, it is critical to search domain-specific hyper-parameters during inference. However, searching for an optimal parameter set is time-consuming. To overcome this, we propose BAHOP, a novel Similarity-based Basin Hopping optimization for fast parameter tuning to enhance inference performance on out-of-domain data. The proposed BAHOP achieves 5\% to 30\% improvement in accuracy with $\times5$ times faster on average.

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Jun Wang, Yu Mao, Yufei Cui, Nan Guan, Chun Jason Xue. 2024-04-17. BAHOP: Similarity-based Basin Hopping for A fast hyper-parameter search in WSI classification. https://arxiv.org/abs/2404.11161

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