BAHOP: Similarity-Based Basin Hopping for A Fast Hyper-Parameter Search in WSI Classification
Wang, Jun Mao, Yu Cui, Yufei Guan, Nan Xue, Chun Jason
Wang, Jun
Mao, Yu
Cui, Yufei
Guan, Nan
Xue, Chun Jason
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Department
Computer Science
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Conference proceeding
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Language
English
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Abstract
Pre-processing whole slide images can impact classification performance. Our study shows that using fixed hyper-parameters for pre-processing out-of-domain WSIs can significantly degrade performance. We suggest 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. Evaluated on Camelyon dataset and TCGA cohorts (COAD, BRCA), the proposed BAHOP achieves 2% to 30% improvement in accuracy with ×5 times faster on average.
Citation
J. Wang, Y. Mao, Y. Cui, N. Guan, C.J. Xue, "BAHOP: Similarity-Based Basin Hopping for A Fast Hyper-Parameter Search in WSI Classification," 2026, pp. 8252-8256.
Source
ICASSP 2026 - 2026 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Conference
2026 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Keywords
46 Information and Computing Sciences, 4605 Data Management and Data Science
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Source
2026 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Publisher
IEEE