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Related Experiment Video

Updated: Sep 19, 2025

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images

Published on: April 21, 2023

Label knowledge guided transformer for automatic radiology report generation.

Rui Wang¹, Jianguo Liang¹

¹College of Computer, Qufu Normal University, Rizhao, 276800, Shandong, China.

Computer Methods and Programs in Biomedicine

|May 31, 2025

Summary

This summary is machine-generated.

This study introduces a novel transformer model to improve AI-generated radiology reports by reducing data bias. The model significantly enhances the accurate identification of abnormal findings in medical images.

Keywords:

Attention mechanism Label features Radiology report Transformer

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Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

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Published on: July 5, 2024

Area of Science:

Artificial Intelligence in Medicine
Medical Imaging Analysis
Natural Language Generation

Background:

Automatic radiology report generation is crucial for clinical decision-making.
Current models suffer from data bias, overshadowing disease-related terms with normal findings.
This bias impacts the accuracy and utility of AI-generated reports.

Purpose of the Study:

To develop a novel model that mitigates data bias in AI-generated radiology reports.
To improve the accurate identification and reporting of abnormal findings.
To enhance the overall quality and reliability of automated medical reporting.

Main Methods:

Proposed a label knowledge guided transformer model incorporating Multi Feature Extraction and Dual-branch Collaborative Attention modules.
Multi Feature Extraction module optimizes label feature extraction using knowledge graphs and clustering, reducing redundant features.
Dual-branch Collaborative Attention module balances visual and label features, preventing direct integration to improve attention allocation.

Main Results:

Achieved state-of-the-art (SOTA) performance on the IU X-Ray and MIMIC-CXR datasets.
Demonstrated an average improvement of 23.3% on IU X-Ray and 20.7% on MIMIC-CXR compared to baseline models.
Validated through six natural language generation evaluation metrics.

Conclusions:

The proposed model effectively captures abnormal features, mitigating data bias.
It shows significant potential for enhancing the quality and accuracy of automated radiology reports.
This advancement can improve clinical workflows and patient care through reliable AI assistance.