Summary of R2gen-mamba: a Selective State Space Model For Radiology Report Generation, by Yongheng Sun et al.
R2Gen-Mamba: A Selective State Space Model for Radiology Report Generation
by Yongheng Sun, Yueh Z. Lee, Genevieve A. Woodard, Hongtu Zhu, Chunfeng Lian, Mingxia Liu
First submitted to arxiv on: 21 Oct 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV); 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 This novel automatic radiology report generation method, R2Gen-Mamba, combines the efficient sequence processing of Mamba with the contextual benefits of Transformer architectures to produce high-quality reports on two benchmark datasets with over 210,000 X-ray image-report pairs. The approach leverages lower computational complexity compared to Transformers, enabling enhanced training and inference efficiency. R2Gen-Mamba outperforms several state-of-the-art methods in terms of report quality and computational efficiency. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates a new way to automatically generate reports for medical imaging. It’s a big help because it can save time and effort for doctors who have to write these reports by hand. The method uses special computer architectures called Mamba and Transformers, which work together to make the report generation faster and more accurate. This helps doctors do their job more efficiently and makes it easier to use the technology in real-world applications. |
Keywords
» Artificial intelligence » Inference » Transformer
