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

ICASSP 2026poster0 citations

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

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.

BibTeX

@inproceedings{icassp2026_bahopsimilarityb,
  title = {BAHOP: Similarity-based Basin Hopping for A fast hyper-parameter search in WSI classification},
  author = {Jun Wang and Yu Mao and Yufei Cui and Nan Guan and Chun Jason Xue},
  booktitle = {ICASSP 2026},
  year = {2026}
}