ChatRadio-Valuer: A Chat Large Language Model for Generalizable
Radiology Report Generation Based on Multi-institution and Multi-system Data
- URL: http://arxiv.org/abs/2310.05242v2
- Date: Tue, 10 Oct 2023 01:22:16 GMT
- Title: ChatRadio-Valuer: A Chat Large Language Model for Generalizable
Radiology Report Generation Based on Multi-institution and Multi-system Data
- Authors: Tianyang Zhong, Wei Zhao, Yutong Zhang, Yi Pan, Peixin Dong, Zuowei
Jiang, Xiaoyan Kui, Youlan Shang, Li Yang, Yaonai Wei, Longtao Yang, Hao
Chen, Huan Zhao, Yuxiao Liu, Ning Zhu, Yiwei Li, Yisong Wang, Jiaqi Yao,
Jiaqi Wang, Ying Zeng, Lei He, Chao Zheng, Zhixue Zhang, Ming Li, Zhengliang
Liu, Haixing Dai, Zihao Wu, Lu Zhang, Shu Zhang, Xiaoyan Cai, Xintao Hu,
Shijie Zhao, Xi Jiang, Xin Zhang, Xiang Li, Dajiang Zhu, Lei Guo, Dinggang
Shen, Junwei Han, Tianming Liu, Jun Liu, Tuo Zhang
- Abstract summary: ChatRadio-Valuer is a tailored model for automatic radiology report generation that learns generalizable representations.
The clinical dataset utilized in this study encompasses a remarkable total of textbf332,673 observations.
ChatRadio-Valuer consistently outperforms state-of-the-art models, especially ChatGPT (GPT-3.5-Turbo) and GPT-4 et al.
- Score: 115.0747462486285
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Radiology report generation, as a key step in medical image analysis, is
critical to the quantitative analysis of clinically informed decision-making
levels. However, complex and diverse radiology reports with cross-source
heterogeneity pose a huge generalizability challenge to the current methods
under massive data volume, mainly because the style and normativity of
radiology reports are obviously distinctive among institutions, body regions
inspected and radiologists. Recently, the advent of large language models (LLM)
offers great potential for recognizing signs of health conditions. To resolve
the above problem, we collaborate with the Second Xiangya Hospital in China and
propose ChatRadio-Valuer based on the LLM, a tailored model for automatic
radiology report generation that learns generalizable representations and
provides a basis pattern for model adaptation in sophisticated analysts' cases.
Specifically, ChatRadio-Valuer is trained based on the radiology reports from a
single institution by means of supervised fine-tuning, and then adapted to
disease diagnosis tasks for human multi-system evaluation (i.e., chest,
abdomen, muscle-skeleton, head, and maxillofacial $\&$ neck) from six different
institutions in clinical-level events. The clinical dataset utilized in this
study encompasses a remarkable total of \textbf{332,673} observations. From the
comprehensive results on engineering indicators, clinical efficacy and
deployment cost metrics, it can be shown that ChatRadio-Valuer consistently
outperforms state-of-the-art models, especially ChatGPT (GPT-3.5-Turbo) and
GPT-4 et al., in terms of the diseases diagnosis from radiology reports.
ChatRadio-Valuer provides an effective avenue to boost model generalization
performance and alleviate the annotation workload of experts to enable the
promotion of clinical AI applications in radiology reports.
Related papers
- MGH Radiology Llama: A Llama 3 70B Model for Radiology [27.575944159578786]
This paper presents an advanced radiology-focused large language model: MGH Radiology Llama.
It is developed using the Llama 3 70B model, building upon previous domain-specific models like Radiology-GPT and Radiology-Llama2.
Our evaluation, incorporating both traditional metrics and a GPT-4-based assessment, highlights the enhanced performance of this work over general-purpose LLMs.
arXiv Detail & Related papers (2024-08-13T01:30:03Z) - Large Model driven Radiology Report Generation with Clinical Quality
Reinforcement Learning [16.849933628738277]
Radiology report generation (RRG) has attracted significant attention due to its potential to reduce the workload of radiologists.
This paper introduces a novel RRG method, textbfLM-RRG, that integrates large models (LMs) with clinical quality reinforcement learning.
Experiments on the MIMIC-CXR and IU-Xray datasets demonstrate the superiority of our method over the state of the art.
arXiv Detail & Related papers (2024-03-11T13:47:11Z) - Consensus, dissensus and synergy between clinicians and specialist
foundation models in radiology report generation [32.26270073540666]
The worldwide shortage of radiologists restricts access to expert care and imposes heavy workloads.
Recent progress in automated report generation with vision-language models offer clear potential in ameliorating the situation.
We build a state-of-the-art report generation system for chest radiographs, $textitFlamingo-CXR, by fine-tuning a well-known vision-language foundation model on radiology data.
arXiv Detail & Related papers (2023-11-30T05:38:34Z) - Radiology Report Generation Using Transformers Conditioned with
Non-imaging Data [55.17268696112258]
This paper proposes a novel multi-modal transformer network that integrates chest x-ray (CXR) images and associated patient demographic information.
The proposed network uses a convolutional neural network to extract visual features from CXRs and a transformer-based encoder-decoder network that combines the visual features with semantic text embeddings of patient demographic information.
arXiv Detail & Related papers (2023-11-18T14:52:26Z) - Radiology-Llama2: Best-in-Class Large Language Model for Radiology [71.27700230067168]
This paper introduces Radiology-Llama2, a large language model specialized for radiology through a process known as instruction tuning.
Quantitative evaluations using ROUGE metrics on the MIMIC-CXR and OpenI datasets demonstrate that Radiology-Llama2 achieves state-of-the-art performance.
arXiv Detail & Related papers (2023-08-29T17:44:28Z) - Radiology-GPT: A Large Language Model for Radiology [74.07944784968372]
We introduce Radiology-GPT, a large language model for radiology.
It demonstrates superior performance compared to general language models such as StableLM, Dolly and LLaMA.
It exhibits significant versatility in radiological diagnosis, research, and communication.
arXiv Detail & Related papers (2023-06-14T17:57:24Z) - Act Like a Radiologist: Radiology Report Generation across Anatomical Regions [50.13206214694885]
X-RGen is a radiologist-minded report generation framework across six anatomical regions.
In X-RGen, we seek to mimic the behaviour of human radiologists, breaking them down into four principal phases.
We enhance the recognition capacity of the image encoder by analysing images and reports across various regions.
arXiv Detail & Related papers (2023-05-26T07:12:35Z) - Self adaptive global-local feature enhancement for radiology report
generation [10.958641951927817]
We propose a novel framework AGFNet to dynamically fuse the global and anatomy region feature to generate multi-grained radiology report.
Firstly, we extract important anatomy region features and global features of input Chest X-ray (CXR)
Then, with the region features and the global features as input, our proposed self-adaptive fusion gate module could dynamically fuse multi-granularity information.
Finally, the captioning generator generates the radiology reports through multi-granularity features.
arXiv Detail & Related papers (2022-11-21T11:50:42Z) - Medical Image Captioning via Generative Pretrained Transformers [57.308920993032274]
We combine two language models, the Show-Attend-Tell and the GPT-3, to generate comprehensive and descriptive radiology records.
The proposed model is tested on two medical datasets, the Open-I, MIMIC-CXR, and the general-purpose MS-COCO.
arXiv Detail & Related papers (2022-09-28T10:27:10Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.