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Author Spotlight: An Efficient and Robust Software for Automated Fusion of Multiple Preclinical Imaging Modalities

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Visual prior-based cross-modal alignment network for radiology report generation.

Sheng Zhang¹, Chuan Zhou¹, Leiting Chen¹

¹Key Laboratory of Digital Media Technology of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, 611731, China.

Computers in Biology and Medicine

|October 11, 2023

Summary

This study introduces a novel Visual Prior-based Cross-modal Alignment Network for automated radiology report generation. The model enhances accuracy by incorporating visual priors and improving image-text alignment, outperforming existing methods.

Keywords:

Contrastive attention Cross-modal alignment Multi-head attention Radiology report generation Visual prior

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Area of Science:

Artificial Intelligence
Medical Imaging
Natural Language Processing

Background:

Automated radiology report generation aims to reduce radiologist workload and improve diagnostic accuracy.
Existing methods struggle with incorporating visual priors and aligning image-text features.

Purpose of the Study:

To develop an advanced automated radiology report generation model addressing limitations of prior approaches.
To improve the identification of abnormal regions and consistency between radiological images and generated reports.

Main Methods:

Proposed a Visual Prior-based Cross-modal Alignment Network.
Introduced Contrastive Attention to extract visual prior (difference information) from images.
Developed a Cross-modal Alignment Network for image-text feature alignment using pre-trained models.
Implemented Visual Prior-guided Multi-Head Attention for integrating visual prior into report generation.

Main Results:

The proposed model demonstrated superior performance on the IU-Xray and MIMIC-CXR datasets.
Achieved high BLEU-4 scores (0.188, 0.116) and CIDEr scores (0.409, 0.240), surpassing state-of-the-art models.
Effectively identified abnormalities and improved image-text consistency.

Conclusions:

The Visual Prior-based Cross-modal Alignment Network significantly advances automated radiology report generation.
Incorporating visual priors and cross-modal alignment enhances model accuracy and reliability.
This approach holds promise for clinical applications in medical imaging analysis.