Summary of Argus: Benchmarking and Enhancing Vision-language Models For 3d Radiology Report Generation, by Che Liu et al.
Argus: Benchmarking and Enhancing Vision-Language Models for 3D Radiology Report Generation
by Che Liu, Zhongwei Wan, Yuqi Wang, Hui Shen, Haozhe Wang, Kangyu Zheng, Mi Zhang, Rossella Arcucci
First submitted to arxiv on: 11 Jun 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 A novel approach to automatic radiology report generation for 3D CT scans is presented in this paper. The authors aim to streamline the labor-intensive process of report writing by radiologists, which has significant potential for improving clinical diagnostics. A comprehensive benchmark for 3D radiograph report generation (3DRRG) was lacking, and optimal training strategies for Vision Language Models (VLMs) were not well understood. To address this gap, the authors curate CT-3DRRG, a large publicly available dataset for evaluating VLM performance on 3DRRG. They also propose a comprehensive training recipe that explores key factors such as vision encoder pretraining strategies and visual token compression. The authors introduce Argus, a state-of-the-art family of VLMs that achieves superior performance across different model sizes and input 3D medical image resolutions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Automatic radiology report generation can help doctors write reports faster and more accurately. This paper helps make this possible by creating a big dataset for testing AI models on writing CT scan reports. The authors also share how to train these AI models best, considering factors like what kind of images they’re trained on and how much data they use. They even introduce a new AI model that does better than others at reading and reporting on 3D CT scans. |
Keywords
» Artificial intelligence » Encoder » Pretraining » Token
