Summary of Anatomically-grounded Fact Checking Of Automated Chest X-ray Reports, by R. Mahmood et al.
Anatomically-Grounded Fact Checking of Automated Chest X-ray Reports
by R. Mahmood, K.C.L. Wong, D. M. Reyes, N. D’Souza, L. Shi, J. Wu, P. Kaviani, M. Kalra, G. Wang, P. Yan, T. Syeda-Mahmood
First submitted to arxiv on: 3 Dec 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 The proposed novel model for explainable fact-checking identifies errors in radiology findings and their locations indicated through reports, leveraging large-scale vision-language models. By analyzing the types of errors made by automated reporting methods, a new synthetic dataset is derived from a ground truth dataset, pairing images with real and fake descriptions of findings and their locations. A multi-label cross-modal contrastive regression network is trained on this dataset to detect errors in reports generated by several state-of-the-art automated reporting tools. Evaluation results show over 40% improvement in report quality through error detection and correction. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper explores a new way to improve medical image analysis reports. Right now, computers can generate reports using only medical images as input, but these reports often have mistakes. The researchers propose a way to fix these errors by creating a model that checks the accuracy of the report. They did this by looking at what kinds of mistakes are made and then created a special dataset with correct and incorrect descriptions of findings from real images. A new computer network is trained on this data to detect errors in reports generated by other AI systems. The results show that this method can improve the quality of medical image analysis reports by over 40%. |
Keywords
» Artificial intelligence » Regression
