Summary of M2ost: Many-to-one Regression For Predicting Spatial Transcriptomics From Digital Pathology Images, by Hongyi Wang et al.
M2OST: Many-to-one Regression for Predicting Spatial Transcriptomics from Digital Pathology Images
by Hongyi Wang, Xiuju Du, Jing Liu, Shuyi Ouyang, Yen-Wei Chen, Lanfen Lin
First submitted to arxiv on: 23 Sep 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 proposes a new approach to predict Spatial Transcriptomics (ST) expressions from digital pathology images. The current methods ignore the multi-scale information in the images, which is crucial for accurate gene expression prediction. The authors introduce M2OST, a many-to-one regression Transformer that uses multiple images to jointly predict gene expressions. This approach can be easily scaled and incorporates nearby inter-spot features, enhancing performance. M2OST achieves state-of-the-art results on three public ST datasets with fewer parameters and floating-point operations (FLOPs). |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about using computers to predict the expression of genes in tumors based on images of the tumors. This helps doctors understand how the tumor works and how it might respond to different treatments. The current way of doing this is expensive, so the authors want to find a better method. They propose a new approach called M2OST that looks at multiple parts of the image together to make predictions. This approach is better than the old one because it uses more information from the image and doesn’t waste any of it. |
Keywords
» Artificial intelligence » Regression » Transformer
