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Related Experiment Video

Updated: Jan 9, 2026

Multicolor 3D Printing of Complex Intracranial Tumors in Neurosurgery
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MultiModal craniocerebral diagnose based on 3D CT and image reports.

Wenxuan He1, Qishen Chen1, Wei Gao2

  • 1Shanghai University, Shanghai, 200444, China.

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|December 1, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces a multimodal cranial diagnosis model (MM-CD) that combines 3D CT scans and imaging reports. The MM-CD model enhances diagnostic accuracy for brain lesions, improving patient care during emergencies.

Keywords:
3D CT imageCraniocerebral CTMedical image diagnoseMedical visual question answeringMulti-scale image fusion

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Area of Science:

  • Medical imaging analysis
  • Artificial intelligence in healthcare
  • Radiology and diagnostic imaging

Background:

  • Current cranial CT diagnostic methods often use single-modal data, leading to information loss or insufficient characterization.
  • Reliance on single CT slices misses cross-slice lesion information, while reports lack pixel-level detail.
  • 3D Convolutional Neural Networks (CNNs) struggle with identifying very small lesions in full 3D scans.

Purpose of the Study:

  • To develop a multimodal diagnostic model (MM-CD) integrating 3D CT findings and imaging reports for improved cranial lesion diagnosis.
  • To address limitations of single-modal approaches and enhance the characterization of small, sparse lesions.
  • To reduce missed-diagnosis rates and shorten treatment times in acute care settings.

Main Methods:

  • A novel multimodal diagnostic model (MM-CD) was developed, integrating 3D CT scans and textual imaging reports.
  • Utilized a 2D pretrained model with a vertical-dimension weight generation module to focus on abnormal CT slices.
  • Implemented a multi-scale image fusion module to consolidate lesion information across slices.
  • Incorporated a self-attention mechanism to integrate CT data with imaging reports for a comprehensive diagnostic reference.

Main Results:

  • The MM-CD model demonstrated improved diagnostic accuracy by integrating multimodal data.
  • Achieved a 1.65% increase in overall accuracy compared to existing state-of-the-art multimodal models on a clinical dataset.
  • Successfully consolidated lesion descriptions from multiple CT slices and enhanced characterization.

Conclusions:

  • The proposed MM-CD model effectively leverages multimodal data for cranial lesion diagnosis.
  • This approach shows significant potential in reducing missed diagnoses of small lesions and improving efficiency in acute care.
  • Multimodal integration offers a promising direction for advancing AI-driven diagnostic tools in medical imaging.