Summary of Focus: Knowledge-enhanced Adaptive Visual Compression For Few-shot Whole Slide Image Classification, by Zhengrui Guo et al.
FOCUS: Knowledge-enhanced Adaptive Visual Compression for Few-shot Whole Slide Image Classification
by Zhengrui Guo, Conghao Xiong, Jiabo Ma, Qichen Sun, Lishuang Feng, Jinzhuo Wang, Hao Chen
First submitted to arxiv on: 22 Nov 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Quantitative Methods (q-bio.QM)
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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 A novel framework for few-shot learning in computational pathology (CPath) is introduced to address the scarcity of expert annotations and patient privacy constraints. The knowledge-enhanced adaptive visual compression framework, dubbed FOCUS, combines pathology foundation models with language prior knowledge to prioritize discriminative regions in whole slide images (WSIs). This approach implements a progressive three-stage compression strategy, leveraging foundation models for global visual redundancy elimination, integrating compressed features with language prompts for semantic relevance assessment, and performing neighbor-aware visual token filtering while preserving spatial coherence. Experimental results on pathological datasets demonstrate superior performance in few-shot pathology diagnosis. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Few-shot learning is important for cancer diagnosis because it helps solve a big problem in medicine. Doctors need to look at many images of tissues from patients, but they don’t have enough time or experts to analyze them all. This makes it hard to find the most important features that help doctors make good diagnoses. A new way to solve this problem is called FOCUS. It uses special computer models and language tools to help doctors focus on the most important parts of the images. This helps them diagnose cancer more quickly and accurately, even when they don’t have much data to work with. |
Keywords
» Artificial intelligence » Few shot » Token
