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Multimodal text guided network for chest CT pneumonia classification.

Yujuan Feng1, Guangyi Huang1, Fujiao Ju2

  • 1College of Computer Science, Beijing University of Technology, Beijing, 100124, China.

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|September 25, 2025
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Summary
This summary is machine-generated.

This study introduces a novel Multi-modal Text-Guided Network (MTGNet) for improved pneumonia diagnosis from CT scans. The model effectively integrates imaging and text data, enhancing classification accuracy for this serious respiratory disease.

Keywords:
Chest CT sequencesMulti-modal contrastive learningPneumonia classification

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Respiratory Medicine

Background:

  • Pneumonia is a widespread respiratory illness with significant global health impact.
  • Deep learning shows promise for automated pneumonia diagnosis from medical images, but current methods face limitations.
  • Slice-based and sequence-based classification methods struggle with spatial context, labor-intensive annotations, and multi-modal information integration.

Purpose of the Study:

  • To develop an advanced deep learning model for accurate pneumonia classification using chest CT sequences.
  • To address the limitations of existing methods by effectively integrating multi-modal information (CT images and textual reports).
  • To enhance feature learning by simulating clinical diagnostic processes and leveraging semantic information from textual descriptions.

Main Methods:

  • Proposed a Multi-modal Text-Guided Network (MTGNet) incorporating a sequential graph pooling network for CT sequence encoding.
  • Developed a CT description encoder to learn from textual reports and a modal transfer module to generate simulated textual features.
  • Employed cross-modal attention for fusing sequence-level and simulated textual representations, alongside contrastive learning for discriminative feature extraction.

Main Results:

  • The MTGNet model demonstrated significant improvements in pneumonia classification performance on a self-constructed dataset.
  • Effective integration of multi-modal information enhanced the model's ability to capture critical spatial and semantic features.
  • The proposed approach successfully simulated clinical diagnostic workflows, leading to superior classification outcomes.

Conclusions:

  • The developed MTGNet offers a powerful new approach for pneumonia classification using chest CT sequences.
  • Integrating textual data with CT imaging significantly enhances diagnostic accuracy.
  • The model's ability to learn discriminative features and simulate clinical reasoning holds promise for advancing automated medical image analysis.