Summary of Memory-based Cross-modal Semantic Alignment Network For Radiology Report Generation, by Yitian Tao et al.
Memory-based Cross-modal Semantic Alignment Network for Radiology Report Generation
by Yitian Tao, Liyan Ma, Jing Yu, Han Zhang
First submitted to arxiv on: 31 Mar 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 proposed model, MCSAM (Memory-based Cross-modal Semantic Alignment Model), tackles the challenge of generating accurate and fluent radiology reports by leveraging a well-initialized long-term clinical memory bank. This approach enables the model to learn disease-related representations and prior knowledge for different modalities, facilitating feature consolidation and cross-modal semantic alignment. The model also incorporates learnable memory tokens that can be seen as prompts, allowing it to memorize state and additional information while generating reports. Extensive experiments demonstrate the promising performance of MCSAM on the MIMIC-CXR dataset. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary MCSAM is a new way for computers to write radiology reports. These reports help doctors diagnose diseases from X-rays and other images. Right now, most computer programs try to translate what’s in an image into words, but it’s hard because there’s not much information about the disease in either the image or the report. MCSAM is better because it uses a special memory bank that knows about different types of diseases and how they relate to each other. This helps the model learn and generate reports that are accurate and easy to understand. |
Keywords
» Artificial intelligence » Alignment
