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Rad-SpatialNet: A Frame-based Resource for Fine-Grained Spatial Relations in Radiology Reports.

Surabhi Datta1, Morgan Ulinski2, Jordan Godfrey-Stovall1

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Summary
This summary is machine-generated.

This study introduces Rad-SpatialNet, a framework for understanding spatial language in radiology reports. It uses frame semantics and BERT models to accurately extract spatial relationships, improving natural language processing for medical imaging analysis.

Keywords:
frame semanticsradiologyspatial relations

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Area of Science:

  • Natural Language Processing (NLP)
  • Computational Linguistics
  • Medical Informatics

Background:

  • Spatial language in radiology reports is complex and nuanced.
  • Existing general-domain spatial representations may not capture specific radiological nuances.
  • Accurate extraction of spatial information is crucial for clinical applications.

Purpose of the Study:

  • To propose Rad-SpatialNet, a novel representation framework for encoding spatial language in radiology.
  • To adapt and improve upon the general-domain SpatialNet framework for radiological use.
  • To develop and evaluate NLP models for extracting fine-grained spatial information from radiology reports.

Main Methods:

  • Developed Rad-SpatialNet based on frame semantics.
  • Created a corpus of 400 annotated radiology reports (chest X-rays, brain MRIs, babygrams).
  • Applied BERT-based models (BERTBASE, BERTLARGE) for spatial trigger and frame element extraction.

Main Results:

  • BERTLARGE achieved an F1 score of 77.89 for spatial trigger extraction.
  • Overall F1 scores of 81.61 (gold triggers) and 66.25 (predicted triggers) for frame element identification.
  • Demonstrated the effectiveness of domain knowledge integration in spatial relation understanding.

Conclusions:

  • Rad-SpatialNet provides an effective framework for representing radiological spatial language.
  • BERT models show promising performance in extracting spatial information from radiology texts.
  • The developed resource can advance NLP applications in medical imaging analysis.