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A Course-Focused Dual Curriculum For Image Captioning.

Mohammad Alsharid1, Rasheed El-Bouri1, Harshita Sharma1

  • 1Institute of Biomedical Engineering, University of Oxford, Oxford, UK.

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|August 20, 2021
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Summary
This summary is machine-generated.

This study introduces a curriculum learning method for fetal ultrasound image captioning. Dynamically transitioning between image and text training modalities improves captioning accuracy compared to simultaneous training.

Keywords:
Image captioningcurriculum learningfetal ultrasoundimage descriptionmeta-learning

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Computer Vision

Background:

  • Fetal ultrasound imaging is crucial for prenatal diagnostics.
  • Accurate image captioning aids in clinical interpretation and data analysis.
  • Current methods may not optimally leverage multi-modal training data.

Purpose of the Study:

  • To develop and evaluate a novel curriculum learning approach for fetal ultrasound image captioning.
  • To investigate the impact of dynamically transitioning between image and text modalities during training.
  • To compare image-first and text-first curriculum strategies.

Main Methods:

  • Proposed a course-focused dual curriculum learning method for image captioning.
  • Implemented two configurations: image-first and text-first curriculum sequences.
  • Trained models to dynamically transition between image and text modalities over training epochs.

Main Results:

  • Dynamically transitioning between text and image modalities significantly improved captioning performance.
  • The course-focused dual curriculum approach outperformed models trained with equal modality consideration per epoch.
  • Both image-first and text-first curriculum strategies showed benefits over baseline methods.

Conclusions:

  • Curriculum learning with dynamic modality transition is an effective strategy for fetal ultrasound image captioning.
  • The proposed method enhances the model's ability to learn from multi-modal data.
  • This approach offers a promising direction for improving AI-driven prenatal diagnostic tools.