Summary of Morphological Prototyping For Unsupervised Slide Representation Learning in Computational Pathology, by Andrew H. Song et al.
Morphological Prototyping for Unsupervised Slide Representation Learning in Computational Pathology
by Andrew H. Song, Richard J. Chen, Tong Ding, Drew F.K. Williamson, Guillaume Jaume, Faisal Mahmood
First submitted to arxiv on: 19 May 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG); Applications (stat.AP)
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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 introduces a novel approach called PANTHER to learn representation of pathology whole-slide images (WSIs) in an unsupervised manner. By leveraging morphological redundancy in tissue, PANTHER aims to build a task-agnostic slide representation that can be used for various downstream tasks. The authors propose a prototype-based approach rooted in the Gaussian mixture model, which summarizes the set of WSI patches into a smaller set of morphological prototypes. This compact representation is then evaluated on 13 datasets for subtyping and survival tasks, demonstrating improved performance compared to supervised Multiple Instance Learning (MIL) baselines. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper tries to improve how computers understand pathology whole-slide images without being told what to look for. Normally, this task uses a method called Multiple Instance Learning, but it only works well when there’s lots of data. The authors think that if they can find patterns in the way tissue looks under a microscope, they can make a computer program that can understand these images without needing lots of training data. They came up with an idea called PANTHER and tested it on many different types of slides. It worked really well! |
Keywords
» Artificial intelligence » Mixture model » Supervised » Unsupervised
