Summary of Summarizing Radiology Reports Findings Into Impressions, by Raul Salles De Padua and Imran Qureshi
Summarizing Radiology Reports Findings into Impressions
by Raul Salles de Padua, Imran Qureshi
First submitted to arxiv on: 10 May 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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 A novel approach to radiology report summarization is presented, utilizing a fine-tuned BERT-to-BERT encoder-decoder model that achieves state-of-the-art performance using a novel method for augmenting medical data. The proposed pipeline processes the MIMIC CXR dataset, enabling future models to build upon this work. This research investigates the limitations of the model and provides insights into radiology knowledge gain. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps doctors quickly summarize complex findings to communicate with specialists and make decisions about which patients have the most urgent cases. By using a special AI model, researchers were able to create a better way to summarize medical reports. The best model was a fine-tuned BERT-to-BERT encoder-decoder that did very well on a test. |
Keywords
» Artificial intelligence » Bert » Encoder decoder » Summarization
