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Knowledge Graph Applications in Medical Imaging Analysis: A Scoping Review.

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Knowledge graphs enhance medical imaging analysis across applications like classification and segmentation. Future research should focus on semi-supervised learning and task-agnostic models to overcome data limitations and improve generalizability.

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Area of Science:

  • Medical Imaging Analysis
  • Artificial Intelligence
  • Knowledge Representation

Background:

  • Structured graphs, particularly knowledge graphs, are increasingly used for efficient knowledge representation in various downstream tasks.
  • Knowledge graphs model prior knowledge using nodes and edges to represent semantically connected entities.
  • These graph structures have been adopted in diverse medical imaging applications.

Purpose of the Study:

  • To systematically review and analyze the application of knowledge graphs in medical imaging analysis.
  • To identify current limitations and propose future research directions in this domain.

Main Methods:

  • A systematic literature search was conducted across five databases.
  • Relevant articles applying knowledge graphs to medical imaging analysis were screened, evaluated, and reviewed.
  • A systematic analysis of the selected literature was performed.

Main Results:

  • Four key applications were identified: disease classification, localization/segmentation, report generation, and image retrieval.
  • Limitations include scarce annotated data and poor generalizability to different tasks.
  • Future directions involve semi-supervised frameworks and task-agnostic models to address these limitations.

Conclusions:

  • This review consolidates state-of-the-art knowledge graph applications in medical imaging.
  • It aims to foster further research by highlighting current challenges and potential solutions.