Summary of Mew: Multiplexed Immunofluorescence Image Analysis Through An Efficient Multiplex Network, by Sukwon Yun et al.
Mew: Multiplexed Immunofluorescence Image Analysis through an Efficient Multiplex Network
by Sukwon Yun, Jie Peng, Alexandro E. Trevino, Chanyoung Park, Tianlong Chen
First submitted to arxiv on: 25 Jul 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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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 framework called Mew, which efficiently processes multiplexed immunofluorescence (mIF) images using graph-based approaches. Mew tackles the limitations of current methodologies by addressing cellular heterogeneity and scalability issues through its multiplex network architecture and Graph Neural Network (GNN) design. The framework consists of two layers: a Voronoi network for geometric information and a Cell-type network for capturing cell-wise homogeneity. This scalable GNN is trained on the entire graph, while an interpretable attention module identifies relevant layers for image classification. Extensive experiments on real-world patient datasets demonstrate Mew’s effectiveness and efficiency in mIF image analysis. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Mew is a new way to analyze images of cells that have different colors (multiplexed immunofluorescence). It uses graphs, which are like maps that show how things are connected. Mew makes these graphs work better by dividing them into two parts: one for the shape of the cells and one for what kind of cells they are. This helps Mew learn from lots of images at once (scalability) and pick out important information (interpretable attention). The results look promising, with Mew doing a good job on real-world patient data. |
Keywords
» Artificial intelligence » Attention » Gnn » Graph neural network » Image classification
