Summary of M2ort: Many-to-one Regression Transformer For Spatial Transcriptomics Prediction From Histopathology Images, by Hongyi Wang et al.
M2ORT: Many-To-One Regression Transformer for Spatial Transcriptomics Prediction from Histopathology Images
by Hongyi Wang, Xiuju Du, Jing Liu, Shuyi Ouyang, Yen-Wei Chen, Lanfen Lin
First submitted to arxiv on: 19 Jan 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Multimedia (cs.MM)
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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 M2ORT, a many-to-one regression Transformer that directly predicts Spatial Transcriptomics (ST) expressions from digital pathology images. It tackles the limitation of existing methods by adopting a decoupled multi-scale feature extractor to accommodate the hierarchical structure of the images. The model accepts multiple images with different magnifications and learns a many-to-one relationship through training, achieving state-of-the-art performance on three public ST datasets while using fewer parameters and floating-point operations (FLOPs). This approach has the potential to reduce the acquisition cost of ST data. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary M2ORT is a new way to predict what’s happening inside tumors from pictures taken with special microscopes. It uses a special kind of computer program called a Transformer, which can look at many different images and figure out how they’re related. This helps it learn to predict where certain genes are turned on or off in the tumor based on just looking at the pictures. The new method works really well and might make it possible to get this information without having to do as much expensive testing. |
Keywords
» Artificial intelligence » Regression » Transformer
