Summary of Bahop: Similarity-based Basin Hopping For a Fast Hyper-parameter Search in Wsi Classification, by Jun Wang et al.
BAHOP: Similarity-based Basin Hopping for A fast hyper-parameter search in WSI classification
by Jun Wang, Yu Mao, Yufei Cui, Nan Guan, Chun Jason Xue
First submitted to arxiv on: 17 Apr 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This study highlights the importance of adapting pre-processing strategies when applying whole slide images (WSIs) to new, unseen domains. Fixed hyper-parameters can lead to significant performance degradation. To address this issue, researchers propose a novel optimization technique called BAHOP, which leverages similarity-based Basin Hopping to efficiently tune parameters and enhance inference accuracy on out-of-domain data. The proposed approach achieves 5% to 30% improvement in accuracy while reducing the processing time by an average of five times. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Pre-processing whole slide images (WSIs) is crucial for accurate classification, but traditional methods can fail when applied to new, unseen domains. Researchers found that using fixed hyper-parameters can significantly decrease performance. To solve this problem, a new approach called BAHOP was developed. It’s like having a special tool that helps find the best settings quickly and accurately. This means better results with less time and effort. |
Keywords
» Artificial intelligence » Classification » Inference » Optimization
