Summary of Fg-cxr: a Radiologist-aligned Gaze Dataset For Enhancing Interpretability in Chest X-ray Report Generation, by Trong Thang Pham et al.
FG-CXR: A Radiologist-Aligned Gaze Dataset for Enhancing Interpretability in Chest X-Ray Report Generation
by Trong Thang Pham, Ngoc-Vuong Ho, Nhat-Tan Bui, Thinh Phan, Patel Brijesh, Donald Adjeroh, Gianfranco Doretto, Anh Nguyen, Carol C. Wu, Hien Nguyen, Ngan Le
First submitted to arxiv on: 23 Nov 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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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 This paper tackles the challenge of developing an interpretable system for generating reports in chest X-ray analysis. The authors propose a novel approach to generate reports that align with the interpretations of real radiologists. They introduce the Fine-Grained CXR dataset, which provides fine-grained paired information between captions generated by radiologists and gaze attention heatmaps. The authors demonstrate that simply applying black-box image captioning methods is insufficient for generating reports that explain which information in CXRs is utilized. Instead, they propose an explainable radiologist’s attention generator network (Gen-XAI) that mimics the diagnosis process of radiologists. The method is evaluated through extensive experiments, and the datasets and checkpoint are available online. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps computers make better reports about chest X-rays. Right now, these reports don’t always match what real doctors would say. To fix this, the authors created a special dataset with information that matches how doctors look at X-rays. They also built a new computer system that tries to think like doctors do when they read X-rays. This system is better than old systems because it explains which parts of the X-ray are important and why. The results show that this new system can make more accurate reports. |
Keywords
» Artificial intelligence » Attention » Image captioning
