Fact-Aware Multimodal Retrieval Augmentation for Accurate Medical Radiology Report Generation

• URL: http://arxiv.org/abs/2407.15268v1
• Date: Sun, 21 Jul 2024 21:04:28 GMT
• Title: Fact-Aware Multimodal Retrieval Augmentation for Accurate Medical Radiology Report Generation
• Authors: Liwen Sun, James Zhao, Megan Han, Chenyan Xiong,
• Abstract summary: We introduce a fact-aware multimodal retrieval-augmented pipeline in generating accurate radiology reports. We first leverage RadGraph to mine factual report pairs, then integrate factual knowledge to train a universal multimodal retriever. Experiments show that our multimodal retriever outperforms state-of-the-art retrievers on both language generation and radiology-specific metrics.
• Score: 14.86028303006519
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Multimodal foundation models hold significant potential for automating radiology report generation, thereby assisting clinicians in diagnosing cardiac diseases. However, generated reports often suffer from serious factual inaccuracy. In this paper, we introduce a fact-aware multimodal retrieval-augmented pipeline in generating accurate radiology reports (FactMM-RAG). We first leverage RadGraph to mine factual report pairs, then integrate factual knowledge to train a universal multimodal retriever. Given a radiology image, our retriever can identify high-quality reference reports to augment multimodal foundation models, thus enhancing the factual completeness and correctness of report generation. Experiments on two benchmark datasets show that our multimodal retriever outperforms state-of-the-art retrievers on both language generation and radiology-specific metrics, up to 6.5% and 2% score in F1CheXbert and F1RadGraph. Further analysis indicates that employing our factually-informed training strategy imposes an effective supervision signal, without relying on explicit diagnostic label guidance, and successfully propagates fact-aware capabilities from the multimodal retriever to the multimodal foundation model in radiology report generation.

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)
• AutoRG-Brain: Grounded Report Generation for Brain MRI [57.22149878985624]
  Radiologists are tasked with interpreting a large number of images in a daily base, with the responsibility of generating corresponding reports. This demanding workload elevates the risk of human error, potentially leading to treatment delays, increased healthcare costs, revenue loss, and operational inefficiencies. We initiate a series of work on grounded Automatic Report Generation (AutoRG) This system supports the delineation of brain structures, the localization of anomalies, and the generation of well-organized findings.
  arXiv  Detail & Related papers  (2024-07-23T17:50:00Z)
• 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)
• MAIRA-1: A specialised large multimodal model for radiology report generation [41.69727330319648]
  We present a radiology-specific multimodal model for generating radiological reports from chest X-rays (CXRs) Our work builds on the idea that large language model(s) can be equipped with multimodal capabilities through alignment with pre-trained vision encoders. Our proposed model (MAIRA-1) leverages a CXR-specific image encoder in conjunction with a fine-tuned large language model based on Vicuna-7B, and text-based data augmentation, to produce reports with state-of-the-art quality.
  arXiv  Detail & Related papers  (2023-11-22T19:45:40Z)
• C^2M-DoT: Cross-modal consistent multi-view medical report generation with domain transfer network [67.97926983664676]
  We propose a cross-modal consistent multi-view medical report generation with a domain transfer network (C2M-DoT) C2M-DoT substantially outperforms state-of-the-art baselines in all metrics.
  arXiv  Detail & Related papers  (2023-10-09T02:31:36Z)
• 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)
• Towards Generalist Foundation Model for Radiology by Leveraging Web-scale 2D&3D Medical Data [66.9359934608229]
  This study aims to initiate the development of Radiology Foundation Model, termed as RadFM. To the best of our knowledge, this is the first large-scale, high-quality, medical visual-language dataset, with both 2D and 3D scans. We propose a new evaluation benchmark, RadBench, that comprises five tasks, including modality recognition, disease diagnosis, visual question answering, report generation and rationale diagnosis.
  arXiv  Detail & Related papers  (2023-08-04T17:00:38Z)
• Radiology Report Generation with a Learned Knowledge Base and Multi-modal Alignment [27.111857943935725]
  We present an automatic, multi-modal approach for report generation from chest x-ray. Our approach features two distinct modules: (i) Learned knowledge base and (ii) Multi-modal alignment. With the aid of both modules, our approach clearly outperforms state-of-the-art methods.
  arXiv  Detail & Related papers  (2021-12-30T10:43:56Z)
• Exploring and Distilling Posterior and Prior Knowledge for Radiology Report Generation [55.00308939833555]
  The PPKED includes three modules: Posterior Knowledge Explorer (PoKE), Prior Knowledge Explorer (PrKE) and Multi-domain Knowledge Distiller (MKD) PoKE explores the posterior knowledge, which provides explicit abnormal visual regions to alleviate visual data bias. PrKE explores the prior knowledge from the prior medical knowledge graph (medical knowledge) and prior radiology reports (working experience) to alleviate textual data bias.
  arXiv  Detail & Related papers  (2021-06-13T11:10:02Z)

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.