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Radiological investigations are paramount in the diagnosis and management of various pulmonary diseases. Two essential investigations are the Pulmonary Angiogram and the Positron Emission Tomography (PET) Scan.
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A Pulmonary Angiogram is an invasive procedure involving injecting a contrast medium through a catheter threaded into the pulmonary artery or the right side of the heart to visualize the pulmonary vasculature. Computed Tomography (CT) scans have mainly replaced this...
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Related Experiment Video

Updated: Mar 29, 2026

Author Spotlight: A 3D Digital Model for the Diagnosis and Treatment of Pulmonary Nodules
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Visual question answering-based image-finding generation for pulmonary nodules on chest CT from structured

Maiko Nagao1, Kaito Urata1, Atsushi Teramoto2

  • 1Graduate School of Science and Engineering, Meijo University, 1-501 Shiogamaguchi, Tempaku-ku, Nagoya City, Aichi, 468-8502, Japan.

International Journal of Computer Assisted Radiology and Surgery
|March 27, 2026
PubMed
Summary
This summary is machine-generated.

This study developed a visual question answering (VQA) dataset for chest computed tomography (CT) images to aid in pulmonary nodule diagnosis. The VQA model effectively generates radiological descriptions, offering interactive diagnostic support for physicians.

Keywords:
Deep learningImage-to-textLung noduleText generationVisual question answering

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Radiology

Background:

  • Accurate interpretation of pulmonary nodules on chest CT is crucial for diagnosis.
  • Current methods often rely on fixed descriptions, limiting interactive diagnostic support.

Purpose of the Study:

  • To construct a visual question answering (VQA) dataset from structured chest CT data.
  • To investigate an image-finding generation method for interactive diagnostic support.

Main Methods:

  • Utilized chest CT images from the LIDC-IDRI dataset.
  • Created a VQA dataset with image findings and questions based on morphological characteristics.
  • Fine-tuned a VQA model on the constructed dataset.

Main Results:

  • The VQA dataset generated natural radiological descriptions.
  • The VQA model achieved a high CIDEr score (3.896).
  • Generated findings showed high agreement with reference findings.

Conclusions:

  • A VQA dataset for chest CT images was successfully constructed.
  • The image-finding generation method is effective for interactive diagnostic support.
  • The system presents findings based on physician interests, enhancing diagnostic capabilities.