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Transformers01:26

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A device that transforms voltages from one value to another using induction is called a transformer. A transformer consists of two separate coils, or windings, wrapped around the same soft iron core. However, they are electrically insulated from each other.
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Transformers can provide desired voltages to a circuit by modifying the number of turns in the secondary windings.
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Related Experiment Video

Updated: Jul 15, 2025

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images
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A New Generative Model for Textual Descriptions of Medical Images Using Transformers Enhanced with Convolutional

Artur Gomes Barreto1, Juliana Martins de Oliveira2, Francisco Nauber Bernardo Gois3

  • 1Graduate Program in Electrical Engineering, Federal University of Ceará, Fortaleza 60455-760, Brazil.

Bioengineering (Basel, Switzerland)
|September 28, 2023
PubMed
Summary
This summary is machine-generated.

Researchers developed a generalist generative model for automatic medical image description, achieving 0.76 accuracy. While promising, generated descriptions may need further refinement for clinical use.

Keywords:
biomedical engineeringdigital image processingnatural language processingtransfer learning

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Area of Science:

  • Artificial Intelligence
  • Medical Imaging
  • Natural Language Processing

Background:

  • Automatic medical image description aids clinical interpretation.
  • Existing research lacks models for diverse modalities and objective quality evaluation.
  • Generalization across medical conditions and image types is a key challenge.

Purpose of the Study:

  • To develop and evaluate a generalist generative model for medical image description.
  • To address the need for model generalization across various image modalities and medical conditions.
  • To improve the objective evaluation of generated medical image descriptions.

Main Methods:

  • A methodological strategy combining natural language processing and medical image feature extraction.
  • Utilizing a generative model based on neural networks.
  • Focusing on achieving model generalization across different image modalities and medical conditions.

Main Results:

  • Promising outcomes in generating medical image descriptions.
  • Achieved an accuracy of 0.7628.
  • Obtained a BLEU-1 score of 0.5387.

Conclusions:

  • The developed model shows potential for automatic medical image description.
  • Generated descriptions may exhibit semantic errors or lack detail, indicating areas for improvement.
  • Data representativeness and chosen techniques influence description quality.