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Related Experiment Video

Updated: Oct 2, 2025

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

Cross Encoder-Decoder Transformer with Global-Local Visual Extractor for Medical Image Captioning.

Hojun Lee¹, Hyunjun Cho¹, Jieun Park¹

¹Department of Computer Science and Engineering, Dongguk University, Seoul 04620, Korea.

Sensors (Basel, Switzerland)

|February 26, 2022

Summary

This summary is machine-generated.

This study introduces novel methods for improved medical image captioning, enhancing text generation from both global and local visual features for more detailed descriptions.

Keywords:

deep learning medical image captioning transformer

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Area of Science:

Medical Imaging
Artificial Intelligence
Computer Vision

Background:

Transformer-based models show promise in image captioning.
Current methods struggle to integrate global image features effectively for comprehensive descriptions.

Purpose of the Study:

To develop advanced methods for generating more accurate and detailed medical image captions.
To address limitations in current image captioning techniques by incorporating both global and local visual information.

Main Methods:

Proposed the Global-Local Visual Extractor (GLVE) to capture comprehensive visual features, including organ size and bone structure, alongside local details like lesion areas.
Introduced the Cross Encoder-Decoder Transformer (CEDT) to integrate multi-level encoder features into the decoding process for richer descriptions.

Main Results:

The proposed model demonstrated superior performance on the IU X-ray dataset compared to existing transformer-based baselines.
Achieved significant improvements in evaluation metrics: 5.6% in BLEU score, 0.56% in METEOR, and 1.98% in ROUGE-L.

Conclusions:

The novel GLVE and CEDT methods enhance medical image captioning by effectively utilizing both global and local visual features.
The proposed approach generates more detailed and accurate descriptions, outperforming previous transformer-based models in key metrics.