Summary of Enhancing Human-computer Interaction in Chest X-ray Analysis Using Vision and Language Model with Eye Gaze Patterns, by Yunsoo Kim et al.
Enhancing Human-Computer Interaction in Chest X-ray Analysis using Vision and Language Model with Eye Gaze Patterns
by Yunsoo Kim, Jinge Wu, Yusuf Abdulle, Yue Gao, Honghan Wu
First submitted to arxiv on: 3 Apr 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
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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 approach enhances human-computer interaction in chest X-ray analysis by incorporating eye gaze data and textual prompts into Vision-Language Models (VLMs). This methodology leverages heatmaps generated from eye gaze data, overlaying them onto medical images to highlight areas of intense radiologist’s focus during evaluation. The results demonstrate that including eye gaze information significantly enhances the accuracy of chest X-ray analysis. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper uses special computers to help doctors with X-ray readings. It makes a new way for these computers and doctors to work together by using where the doctor looks at the X-ray picture. This helps the computer learn more from the doctor’s expertise. The results show that this method is better than other ways of doing it, which could lead to better X-ray reading in the future. |
