放射学图像上的大规模长尾疾病诊断

IF 14.7 1区 综合性期刊 Q1 MULTIDISCIPLINARY SCIENCES
Qiaoyu Zheng, Weike Zhao, Chaoyi Wu, Xiaoman Zhang, Lisong Dai, Hengyu Guan, Yuehua Li, Ya Zhang, Yanfeng Wang, Weidi Xie
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引用次数: 0

摘要

开发全科放射诊断系统可大大提高临床诊断水平。在本文中,我们介绍了 RadDiag,这是一种支持各种模式和解剖的二维和三维输入的基础模型,使用基于变压器的融合模块进行综合疾病诊断。由于患者隐私问题和大规模放射诊断数据集的缺乏,我们利用了在线提供的高质量、经临床医生审查并带有诊断标签的放射图像。我们的数据集 RP3D-DiagDS 包含 40936 个病例,195010 次扫描,涵盖 5568 种疾病(930 个唯一的 ICD-10-CM 代码)。通过实验,我们的 RadDiag 在知识增强策略的内部评估中达到了 95.14% 的 AUC。此外,RadDiag 还可以在来自不同医疗中心的外部诊断数据集上进行零扫描应用或微调,显示出最先进的效果。总之,我们的研究表明,互联网上公开共享的医疗数据是一种巨大而宝贵的资源,有可能为医疗领域建立强大的图像理解模型提供支持。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Large-scale long-tailed disease diagnosis on radiology images

Large-scale long-tailed disease diagnosis on radiology images

Developing a generalist radiology diagnosis system can greatly enhance clinical diagnostics. In this paper, we introduce RadDiag, a foundational model supporting 2D and 3D inputs across various modalities and anatomies, using a transformer-based fusion module for comprehensive disease diagnosis. Due to patient privacy concerns and the lack of large-scale radiology diagnosis datasets, we utilize high-quality, clinician-reviewed radiological images available online with diagnosis labels. Our dataset, RP3D-DiagDS, contains 40,936 cases with 195,010 scans covering 5568 disorders (930 unique ICD-10-CM codes). Experimentally, our RadDiag achieves 95.14% AUC on internal evaluation with the knowledge-enhancement strategy. Additionally, RadDiag can be zero-shot applied or fine-tuned to external diagnosis datasets sourced from various medical centers, demonstrating state-of-the-art results. In conclusion, we show that publicly shared medical data on the Internet is a tremendous and valuable resource that can potentially support building strong models for image understanding in healthcare.

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来源期刊
Nature Communications
Nature Communications Biological Science Disciplines-
CiteScore
24.90
自引率
2.40%
发文量
6928
审稿时长
3.7 months
期刊介绍: Nature Communications, an open-access journal, publishes high-quality research spanning all areas of the natural sciences. Papers featured in the journal showcase significant advances relevant to specialists in each respective field. With a 2-year impact factor of 16.6 (2022) and a median time of 8 days from submission to the first editorial decision, Nature Communications is committed to rapid dissemination of research findings. As a multidisciplinary journal, it welcomes contributions from biological, health, physical, chemical, Earth, social, mathematical, applied, and engineering sciences, aiming to highlight important breakthroughs within each domain.
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