Wang Song, Lin Mingquan, Ghosal Tirthankar, Ding Ying, Peng Yifan
The University of Texas at Austin, Austin, USA.
Population Health Sciences, Weill Cornell Medicine, New York, USA.
Health Data Sci. 2022;2022. doi: 10.34133/2022/9841548. Epub 2022 Jun 14.
There is an increasing trend to represent domain knowledge in structured graphs, which provide efficient knowledge representations for many downstream tasks. Knowledge graphs are widely used to model prior knowledge in the form of nodes and edges to represent semantically connected knowledge entities, which several works have adopted into different medical imaging applications.
We systematically searched over five databases to find relevant articles that applied knowledge graphs to medical imaging analysis. After screening, evaluating, and reviewing the selected articles, we performed a systematic analysis.
We looked at four applications in medical imaging analysis, including disease classification, disease localization and segmentation, report generation, and image retrieval. We also identified limitations of current work, such as the limited amount of available annotated data and weak generalizability to other tasks. We further identified the potential future directions according to the identified limitations, including employing semisupervised frameworks to alleviate the need for annotated data and exploring task-agnostic models to provide better generalizability.
We hope that our article will provide the readers with aggregated documentation of the state-of-the-art knowledge graph applications for medical imaging to encourage future research.
以结构化图形表示领域知识的趋势日益增长，这为许多下游任务提供了高效的知识表示。知识图谱被广泛用于以节点和边的形式对先验知识进行建模，以表示语义上相连的知识实体，已有多项工作将其应用于不同的医学成像应用中。
我们系统地检索了五个数据库，以查找将知识图谱应用于医学成像分析的相关文章。在对所选文章进行筛选、评估和评审后，我们进行了系统分析。
我们研究了医学成像分析中的四个应用，包括疾病分类、疾病定位与分割、报告生成和图像检索。我们还确定了当前工作的局限性，例如可用标注数据量有限以及对其他任务的泛化能力较弱。我们根据所确定的局限性进一步确定了未来的潜在方向，包括采用半监督框架以减少对标注数据的需求，以及探索与任务无关的模型以提供更好的泛化能力。
我们希望本文能为读者提供有关医学成像领域最新知识图谱应用的汇总文档，以鼓励未来的研究。
