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Leveraging an Image-Enhanced Cross-Modal Fusion Network for Radiology Report Generation.

Yi Guo1, Xiaodi Hou1, Zhi Liu1

  • 1School of Information Science and Technology, Dalian Maritime University, Dalian, China.

Journal of Computational Biology : a Journal of Computational Molecular Cell Biology
|August 11, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces the Image-Enhanced Cross-Modal Fusion Network (IFNet) for automated radiology report generation (RRG). IFNet improves the accuracy and efficiency of generating medical reports from X-ray images, even low-quality ones.

Keywords:
medical image enhancementradiology report generationseparable self-attention

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Area of Science:

  • Artificial Intelligence in Medical Imaging
  • Natural Language Processing for Healthcare
  • Radiology and Diagnostic Imaging

Background:

  • Automated radiology report generation (RRG) aims to assist radiologists, improve diagnostic accuracy, and optimize resource allocation.
  • Existing RRG methods often struggle with low-quality images, lack of cross-modal information integration, and high latency.
  • There is a need for advanced models that can enhance feature extraction from suboptimal medical images and efficiently generate reports.

Purpose of the Study:

  • To develop an advanced model, the Image-Enhanced Cross-Modal Fusion Network (IFNet), for automatic radiology report generation.
  • To address limitations in current RRG by enhancing feature extraction from low-quality images and incorporating cross-modal interactions.
  • To improve the efficiency and suitability of RRG models for low-resource environments.

Main Methods:

  • Proposed the Image-Enhanced Cross-Modal Fusion Network (IFNet) comprising three key modules.
  • An image enhancement module to improve the representation of structures in X-ray images.
  • Cross-modal fusion networks to capture interactions between image and text features.
  • An efficient transformer-based module for optimized report generation, suitable for low-resource devices.

Main Results:

  • IFNet demonstrated significant improvements in radiology report generation compared to existing state-of-the-art methods.
  • The image enhancement module successfully boosted the detection rates by improving the detailed representation of image structures.
  • Experimental results on the IU X-ray and MIMIC-CXR datasets validated the effectiveness of IFNet.

Conclusions:

  • The proposed IFNet effectively addresses key challenges in automatic RRG, including low-quality image analysis and efficient report generation.
  • IFNet offers a promising solution for enhancing the capabilities of computer-aided diagnostic tools in radiology.
  • The model's efficiency makes it suitable for deployment in resource-constrained healthcare settings.