Summary of Clinical Context-aware Radiology Report Generation From Medical Images Using Transformers, by Sonit Singh
Clinical Context-aware Radiology Report Generation from Medical Images using Transformers
by Sonit Singh
First submitted to arxiv on: 21 Aug 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: 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 paper explores the application of transformer models in natural language processing (NLP) for generating radiology reports from chest X-rays. The authors highlight limitations in evaluating report generation using standard language metrics and propose a transformer-based architecture for this task. Experimental results on the IU-CXR dataset show superior performance compared to an LSTM-based model, with faster inference times. The study emphasizes the importance of considering both language generation metrics and classification metrics when evaluating radiology report generation systems. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper uses special computer models called transformers to help doctors write reports about chest X-rays. Right now, these reports are often written by hand or using simple templates. The authors show that transformer models can do a much better job of writing these reports, and they can even write them faster than other methods. The study also suggests that we should use two different ways to measure how good the reports are: one way looks at how well the report is written, and the other way checks if the doctor would agree with the diagnosis or treatment plan. |
Keywords
» Artificial intelligence » Classification » Inference » Lstm » Natural language processing » Nlp » Transformer
