视网膜基础语言图像模型(FLAIR):在文本监督中编码专家知识

IF 10.7 1区 医学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Julio Silva-Rodríguez , Hadi Chakor , Riadh Kobbi , Jose Dolz , Ismail Ben Ayed
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引用次数: 0

摘要

基础视觉语言模型目前正在改变计算机视觉,并因其极具前景的泛化能力而在医学成像领域兴起。然而,与其他领域相比,将这一新范式应用于医学影像领域的初步尝试并没有取得令人印象深刻的效果,原因在于医学影像任务所固有的重大领域转变和复杂的专家领域知识。出于对领域专家基础模型的需求,我们提出了 FLAIR,一种用于通用视网膜眼底图像理解的预训练视觉语言模型。为此,我们从不同来源汇编了 38 个开放访问的眼底成像数据集,其中大部分是分类数据集,包含多达 101 种不同的目标条件和 288,307 幅图像。在预训练和零次推理过程中,我们以描述性文本提示的形式整合了专家的领域知识,从而加强了对信息量较少的数据的分类监督。这种文本专家知识是我们从相关临床文献和社区标准中整理出来的,描述了病理的细粒度特征以及它们之间的层次和依赖关系。我们报告了综合评估结果,这些结果说明了在领域转移或未见类别的困难情况下,整合专家知识的好处以及 FLAIR 强大的泛化能力。当使用轻量级线性探针进行调整时,FLAIR 的表现优于经过全面训练的、以数据集为中心的模型,尤其是在少数几种情况下。有趣的是,FLAIR在很大程度上优于更大规模的通用图像语言模型和视网膜特定领域自监督网络,这强调了嵌入专家领域知识的潜力以及通用模型在医学成像中的局限性。预训练模型可在以下网址获取:https://github.com/jusiro/FLAIR。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Foundation Language-Image Model of the Retina (FLAIR): encoding expert knowledge in text supervision
Foundation vision-language models are currently transforming computer vision, and are on the rise in medical imaging fueled by their very promising generalization capabilities. However, the initial attempts to transfer this new paradigm to medical imaging have shown less impressive performances than those observed in other domains, due to the significant domain shift and the complex, expert domain knowledge inherent to medical-imaging tasks. Motivated by the need for domain-expert foundation models, we present FLAIR, a pre-trained vision-language model for universal retinal fundus image understanding. To this end, we compiled 38 open-access, mostly categorical fundus imaging datasets from various sources, with up to 101 different target conditions and 288,307 images. We integrate the expert’s domain knowledge in the form of descriptive textual prompts, during both pre-training and zero-shot inference, enhancing the less-informative categorical supervision of the data. Such a textual expert’s knowledge, which we compiled from the relevant clinical literature and community standards, describes the fine-grained features of the pathologies as well as the hierarchies and dependencies between them. We report comprehensive evaluations, which illustrate the benefit of integrating expert knowledge and the strong generalization capabilities of FLAIR under difficult scenarios with domain shifts or unseen categories. When adapted with a lightweight linear probe, FLAIR outperforms fully-trained, dataset-focused models, more so in the few-shot regimes. Interestingly, FLAIR outperforms by a wide margin larger-scale generalist image-language models and retina domain-specific self-supervised networks, which emphasizes the potential of embedding experts’ domain knowledge and the limitations of generalist models in medical imaging. The pre-trained model is available at: https://github.com/jusiro/FLAIR.
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来源期刊
Medical image analysis
Medical image analysis 工程技术-工程:生物医学
CiteScore
22.10
自引率
6.40%
发文量
309
审稿时长
6.6 months
期刊介绍: Medical Image Analysis serves as a platform for sharing new research findings in the realm of medical and biological image analysis, with a focus on applications of computer vision, virtual reality, and robotics to biomedical imaging challenges. The journal prioritizes the publication of high-quality, original papers contributing to the fundamental science of processing, analyzing, and utilizing medical and biological images. It welcomes approaches utilizing biomedical image datasets across all spatial scales, from molecular/cellular imaging to tissue/organ imaging.
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