Large Model driven Radiology Report Generation with Clinical Quality Reinforcement Learning

• URL: http://arxiv.org/abs/2403.06728v1
• Date: Mon, 11 Mar 2024 13:47:11 GMT
• Title: Large Model driven Radiology Report Generation with Clinical Quality Reinforcement Learning
• Authors: Zijian Zhou, Miaojing Shi, Meng Wei, Oluwatosin Alabi, Zijie Yue, Tom Vercauteren
• Abstract summary: 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.
• Score: 16.849933628738277
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Radiology report generation (RRG) has attracted significant attention due to its potential to reduce the workload of radiologists. Current RRG approaches are still unsatisfactory against clinical standards. This paper introduces a novel RRG method, \textbf{LM-RRG}, that integrates large models (LMs) with clinical quality reinforcement learning to generate accurate and comprehensive chest X-ray radiology reports. Our method first designs a large language model driven feature extractor to analyze and interpret different regions of the chest X-ray image, emphasizing specific regions with medical significance. Next, based on the large model's decoder, we develop a multimodal report generator that leverages multimodal prompts from visual features and textual instruction to produce the radiology report in an auto-regressive way. Finally, to better reflect the clinical significant and insignificant errors that radiologists would normally assign in the report, we introduce a novel clinical quality reinforcement learning strategy. It utilizes the radiology report clinical quality (RadCliQ) metric as a reward function in the learning process. Extensive experiments on the MIMIC-CXR and IU-Xray datasets demonstrate the superiority of our method over the state of the art.

Related papers

• Resource-Efficient Medical Report Generation using Large Language Models [3.2627279988912194]
  Medical report generation is the task of automatically writing radiology reports for chest X-ray images. We propose a new framework leveraging vision-enabled Large Language Models (LLM) for the task of medical report generation.
  arXiv  Detail & Related papers  (2024-10-21T05:08:18Z)
• Multi-modality Regional Alignment Network for Covid X-Ray Survival Prediction and Report Generation [36.343753593390254]
  This study proposes Multi-modality Regional Alignment Network (MRANet), an explainable model for radiology report generation and survival prediction. MRANet visually grounds region-specific descriptions, providing robust anatomical regions with a completion strategy. A cross LLMs alignment is employed to enhance the image-to-text transfer process, resulting in sentences rich with clinical detail and improved explainability for radiologist.
  arXiv  Detail & Related papers  (2024-05-23T02:41:08Z)
• 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)
• Beyond Images: An Integrative Multi-modal Approach to Chest X-Ray Report Generation [47.250147322130545]
  Image-to-text radiology report generation aims to automatically produce radiology reports that describe the findings in medical images. Most existing methods focus solely on the image data, disregarding the other patient information accessible to radiologists. We present a novel multi-modal deep neural network framework for generating chest X-rays reports by integrating structured patient data, such as vital signs and symptoms, alongside unstructured clinical notes.
  arXiv  Detail & Related papers  (2023-11-18T14:37:53Z)
• ChatRadio-Valuer: A Chat Large Language Model for Generalizable Radiology Report Generation Based on Multi-institution and Multi-system Data [115.0747462486285]
  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.
  arXiv  Detail & Related papers  (2023-10-08T17:23:17Z)
• 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)
• Longitudinal Data and a Semantic Similarity Reward for Chest X-Ray Report Generation [7.586632627817609]
  Radiologists face high burnout rates, partly due to the increasing volume of Chest X-rays (CXRs) requiring interpretation and reporting. Our proposed CXR report generator integrates elements of the workflow and introduces a novel reward for reinforcement learning. Results from our study demonstrate that the proposed model generates reports that are more aligned with radiologists' reports than state-of-the-art models.
  arXiv  Detail & Related papers  (2023-07-19T05:41:14Z)
• 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)
• Generative Residual Attention Network for Disease Detection [51.60842580044539]
  We present a novel approach for disease generation in X-rays using a conditional generative adversarial learning. We generate a corresponding radiology image in a target domain while preserving the identity of the patient. We then use the generated X-ray image in the target domain to augment our training to improve the detection performance.
  arXiv  Detail & Related papers  (2021-10-25T14:15:57Z)

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.