Summary of Advances in Multiple Instance Learning For Whole Slide Image Analysis: Techniques, Challenges, and Future Directions, by Jun Wang et al.
Advances in Multiple Instance Learning for Whole Slide Image Analysis: Techniques, Challenges, and Future Directions
by Jun Wang, Yu Mao, Nan Guan, Chun Jason Xue
First submitted to arxiv on: 18 Aug 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 The paper surveys the application of Multiple Instance Learning (MIL) in Whole Slide Image (WSI) analysis for cancer classification and detection. It discusses the challenges and methodologies associated with MIL, including attention mechanisms, pseudo-labeling, transformers, pooling functions, and graph neural networks. The survey also explores the potential of MIL in discovering cancer cell morphology, constructing interpretable machine learning models, and quantifying cancer grading. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how to use computer vision to analyze huge digital images of tissue samples that pathologists use to diagnose cancer. It talks about a special way of doing this called Multiple Instance Learning (MIL) which is good for identifying patterns in these big images. The paper also mentions how MIL can help doctors figure out what kind of cells they’re looking at and understand why certain types of cancer are more likely to occur. |
Keywords
» Artificial intelligence » Attention » Classification » Machine learning
