Summary of Large Language Model with Region-guided Referring and Grounding For Ct Report Generation, by Zhixuan Chen et al.
Large Language Model with Region-guided Referring and Grounding for CT Report Generation
by Zhixuan Chen, Yequan Bie, Haibo Jin, Hao Chen
First submitted to arxiv on: 23 Nov 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 This paper proposes Reg2RG, a novel framework for computed tomography (CT) report generation that focuses on specific regions within the volume, enhancing diagnostic performance by capturing local features. The method utilizes masks from universal segmentation modules to capture local features and integrates them with global features to capture inter-regional relationships. Additionally, the paper introduces a region-report alignment training strategy that leverages recognizing referring regions to guide report generation, improving interpretability. The model is further augmented with a large language model decoder for generating reports from integrated visual features. Experimental results on two large-scale chest CT-report datasets demonstrate the superiority of Reg2RG over state-of-the-art methods in terms of natural language generation and clinical efficacy metrics while preserving promising interpretability. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps computers generate reports about CT scans, which are important tools for doctors to understand what’s going on inside our bodies. Current methods don’t do a great job because they focus too much on the whole picture instead of specific parts. The researchers created a new way called Reg2RG that looks at different regions in the scan and combines that with information from the whole scan. They also developed a special training method to make sure the reports are accurate and easy to understand. The results show that their approach is better than others at creating good reports that doctors can use. |
Keywords
» Artificial intelligence » Alignment » Decoder » Large language model
