Summary of A Disease Labeler For Chinese Chest X-ray Report Generation, by Mengwei Wang et al.
A Disease Labeler for Chinese Chest X-Ray Report Generation
by Mengwei Wang, Ruixin Yan, Zeyi Hou, Ning Lang, Xiuzhuang Zhou
First submitted to arxiv on: 18 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Image and Video Processing (eess.IV)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 addresses the scarcity of Chinese chest X-ray report datasets by proposing a disease labeler tailored for generating such reports. The labeler leverages dual BERT architecture to handle diagnostic reports and clinical information separately, constructing a hierarchical label learning algorithm based on affiliations between diseases and body parts. This approach enhances text classification performance. A dataset consisting of 51,262 report samples was established using this labeler. Experiments validated the effectiveness of the proposed disease labeler. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps create technology for generating Chinese chest X-ray reports by making a special tool to identify diseases from these reports. The tool uses a special computer model called BERT and combines information about diseases and body parts to make it better at recognizing diseases in reports. This was tested on a big dataset of 51,262 report samples and showed that the tool works well. |
Keywords
» Artificial intelligence » Bert » Text classification
