Interactive dual-stream contrastive learning for radiology report generation

IF 4 2区 医学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
Ziqi Zhang, Ailian Jiang
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引用次数: 0

Abstract

Radiology report generation automates diagnostic narrative synthesis from medical imaging data. Current report generation methods primarily employ knowledge graphs for image enhancement, neglecting the interpretability and guiding function of the knowledge graphs themselves. Additionally, few approaches leverage the stable modal alignment information from multimodal pre-trained models to facilitate the generation of radiology reports. We propose the Terms-Guided Radiology Report Generation (TGR), a simple and practical model for generating reports guided primarily by anatomical terms. Specifically, we utilize a dual-stream visual feature extraction module comprised of detail extraction module and a frozen multimodal pre-trained model to separately extract visual detail features and semantic features. Furthermore, a Visual Enhancement Module (VEM) is proposed to further enrich the visual features, thereby facilitating the generation of a list of anatomical terms. We integrate anatomical terms with image features and proceed to engage contrastive learning with frozen text embeddings, utilizing the stable feature space from these embeddings to boost modal alignment capabilities further. Our model incorporates the capability for manual input, enabling it to generate a list of organs for specifically focused abnormal areas or to produce more accurate single-sentence descriptions based on selected anatomical terms. Comprehensive experiments demonstrate the effectiveness of our method in report generation tasks, our TGR-S model reduces training parameters by 38.9% while performing comparably to current state-of-the-art models, and our TGR-B model exceeds the best baseline models across multiple metrics.

Abstract Image

用于生成放射学报告的交互式双流对比学习
放射学报告生成可自动根据医学影像数据进行诊断叙述综合。目前的报告生成方法主要采用知识图谱来增强图像,而忽略了知识图谱本身的可解释性和指导功能。此外,很少有方法利用多模态预训练模型的稳定模态配准信息来促进放射报告的生成。我们提出的术语指导放射报告生成(TGR)是一种简单实用的模型,主要以解剖术语为指导生成报告。具体来说,我们利用由细节提取模块和冷冻多模态预训练模型组成的双流视觉特征提取模块,分别提取视觉细节特征和语义特征。此外,我们还提出了视觉增强模块(VEM)来进一步丰富视觉特征,从而促进解剖术语列表的生成。我们将解剖术语与图像特征整合在一起,然后使用冻结文本嵌入进行对比学习,利用这些嵌入的稳定特征空间进一步提高模态配准能力。我们的模型具有手动输入功能,可针对特定的异常区域生成器官列表,或根据选定的解剖术语生成更准确的单句描述。综合实验证明了我们的方法在报告生成任务中的有效性,我们的 TGR-S 模型减少了 38.9% 的训练参数,同时与当前最先进的模型性能相当,而我们的 TGR-B 模型在多个指标上都超过了最佳基线模型。
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来源期刊
Journal of Biomedical Informatics
Journal of Biomedical Informatics 医学-计算机:跨学科应用
CiteScore
8.90
自引率
6.70%
发文量
243
审稿时长
32 days
期刊介绍: The Journal of Biomedical Informatics reflects a commitment to high-quality original research papers, reviews, and commentaries in the area of biomedical informatics methodology. Although we publish articles motivated by applications in the biomedical sciences (for example, clinical medicine, health care, population health, and translational bioinformatics), the journal emphasizes reports of new methodologies and techniques that have general applicability and that form the basis for the evolving science of biomedical informatics. Articles on medical devices; evaluations of implemented systems (including clinical trials of information technologies); or papers that provide insight into a biological process, a specific disease, or treatment options would generally be more suitable for publication in other venues. Papers on applications of signal processing and image analysis are often more suitable for biomedical engineering journals or other informatics journals, although we do publish papers that emphasize the information management and knowledge representation/modeling issues that arise in the storage and use of biological signals and images. System descriptions are welcome if they illustrate and substantiate the underlying methodology that is the principal focus of the report and an effort is made to address the generalizability and/or range of application of that methodology. Note also that, given the international nature of JBI, papers that deal with specific languages other than English, or with country-specific health systems or approaches, are acceptable for JBI only if they offer generalizable lessons that are relevant to the broad JBI readership, regardless of their country, language, culture, or health system.
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