mlsanityData to 13 Oct 2026

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

arXiv 2404.11161 ↗First posted 17 Apr 2024cs.CV cs.LGcited by 0

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Abstract (arXiv)

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 ×5 times faster on average.

Key facts. No citing papers in the map yet (data through 13 Oct 2026). Builds on 10 of its 32 references that are in the map. First posted on arXiv 17 Apr 2024.

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10 of 32 references.

References · 10

  1. 2020Data Efficient and Weakly Supervised Computational Pathology on Whole Slide Imagescited by 739
  2. 2021TransMIL: Transformer based Correlated Multiple Instance Learning for Whole Slide Image Classificationcited by 551
  3. 2020Dual-stream Multiple Instance Learning Network for Whole Slide Image Classification with Self-supervised Contrastive Learningcited by 474
  4. 2018BOHB: Robust and Efficient Hyperparameter Optimization at Scalecited by 467
  5. 2019Transfusion: Understanding Transfer Learning for Medical Imagingcited by 367
  6. 2022DTFD-MIL: Double-Tier Feature Distillation Multiple Instance Learning for Histopathology Whole Slide Image Classificationcited by 255
  7. 2019Quantifying the effects of data augmentation and stain color normalization in convolutional neural networks for computational pathologycited by 172
  8. 2023Multiple Instance Learning Framework with Masked Hard Instance Mining for Whole Slide Image Classificationcited by 59
  9. 2022Additive MIL: Intrinsically Interpretable Multiple Instance Learning for Pathologycited by 58
  10. 2023Computational Pathology: A Survey Review and The Way Forwardcited by 30

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