Summary of Cxpmrg-bench: Pre-training and Benchmarking For X-ray Medical Report Generation on Chexpert Plus Dataset, by Xiao Wang et al.
CXPMRG-Bench: Pre-training and Benchmarking for X-ray Medical Report Generation on CheXpert Plus Dataset
by Xiao Wang, Fuling Wang, Yuehang Li, Qingchuan Ma, Shiao Wang, Bo Jiang, Chuanfu Li, Jin Tang
First submitted to arxiv on: 1 Oct 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- 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 benchmark for X-ray image-based medical report generation (MRG) aims to overcome the limitations of existing datasets and models in this field. The CheXpert Plus dataset, while a significant advancement, lacks comparative evaluation algorithms and results. To address this challenge, the authors conduct a comprehensive benchmarking of mainstream X-ray report generation models and large language models (LLMs) on the CheXpert Plus dataset. This provides a solid comparative basis for subsequent algorithms and serves as a guide for researchers to quickly grasp the state-of-the-art models in this field. Furthermore, the authors propose a large model for X-ray image report generation using a multi-stage pre-training strategy, including self-supervised autoregressive generation and Xray-report contrastive learning, and supervised fine-tuning. Experimental results show that the proposed approach effectively encodes X-ray images and achieves better performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary X-ray image-based medical report generation is an important area of artificial intelligence that can help reduce diagnostic burdens and patient wait times. The problem with this task is that there aren’t many datasets to compare different algorithms, making it hard for researchers to train and test their models. To fix this issue, the authors compared existing X-ray report generation models and large language models on a new dataset called CheXpert Plus. They also proposed a new way to train models using a combination of self-supervised learning and supervised fine-tuning. The results show that this approach is effective in generating medical reports from X-ray images. |
Keywords
» Artificial intelligence » Autoregressive » Fine tuning » Self supervised » Supervised
