Summary of A Dual-view Approach to Classifying Radiology Reports by Co-training, By Yutong Han et al.
A Dual-View Approach to Classifying Radiology Reports by Co-Training
by Yutong Han, Yan Yuan, Lili Mou
First submitted to arxiv on: 10 Jun 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); 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 novel insight reveals that the structure of radiology reports offers different views of a radiology scan. The Findings and Impression sections provide distinct perspectives, which are leveraged by two machine learning models built on these sections in a co-training approach. This semi-supervised method utilizes massive unlabeled data to boost performance, surpassing competing methods. In a public health surveillance study, the results demonstrate improved performance using the dual views. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Radiology reports contain valuable information that can aid public health initiatives. Researchers have been exploring ways to analyze these reports. A new approach uses two machine learning models built on different parts of the report (the Findings and Impression sections). These models learn from each other, allowing them to work well with large amounts of data they haven’t seen before. This method is better than others at analyzing radiology reports. It was tested in a project tracking public health and showed promising results. |
Keywords
» Artificial intelligence » Machine learning » Semi supervised » Tracking
