Summary of Histgen: Histopathology Report Generation Via Local-global Feature Encoding and Cross-modal Context Interaction, by Zhengrui Guo et al.
HistGen: Histopathology Report Generation via Local-Global Feature Encoding and Cross-modal Context Interaction
by Zhengrui Guo, Jiabo Ma, Yingxue Xu, Yihui Wang, Liansheng Wang, Hao Chen
First submitted to arxiv on: 8 Mar 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); 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 The proposed HistGen framework utilizes multiple instance learning to automate histopathology report generation, aiming to enhance clinical efficiency and alleviate the burden on pathologists. The framework consists of two modules: a local-global hierarchical encoder for visual feature aggregation from region-to-slide perspective, and a cross-modal context module to facilitate alignment between WSIs and diagnostic reports. Experimental results show that HistGen outperforms state-of-the-art models in WSI report generation, and its transfer learning capabilities are demonstrated through superior performance on cancer subtyping and survival analysis tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary HistGen is a new way to help doctors write medical reports about cancer cells more quickly and accurately. It uses special computer algorithms to look at pictures of cancer cells and then write the report based on what it sees. This can make it easier for doctors to make decisions about how to treat patients, and it can also help them understand more about how cancer works. |
Keywords
* Artificial intelligence * Alignment * Encoder * Transfer learning
