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MEDI-SLATE: medical imaging slide-lecture aligned teaching ensemble.

Motaleb Hossen Manik1, Zabirul Islam1, Ge Wang2

  • 1Department of Computer Science, Rensselaer Polytechnic Institute, Troy, NY 12180, United States.

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|April 14, 2026
PubMed
Summary

A new dataset, MEDI-SLATE, pairs medical imaging lecture slides with narration for educational research. This resource supports multimodal learning and AI tool development in medical imaging education.

Keywords:
Educational datasetMedical imagingMedical imaging educationMultimodal learningVision-language model

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Area of Science:

  • Biomedical Engineering
  • Medical Imaging Education
  • Educational Technology

Background:

  • Undergraduate medical imaging education relies heavily on slide-based lectures.
  • Existing resources lack openly available, aligned slide-narration datasets for research.
  • There is a need for high-quality, structured data to advance medical imaging pedagogy.

Purpose of the Study:

  • To introduce MEDI-SLATE, a novel dataset of medical imaging lecture slides paired with refined narration.
  • To provide a comprehensive resource for research in medical imaging education and multimodal learning.
  • To facilitate the development of AI-assisted instructional tools for medical imaging.

Main Methods:

  • Constructed MEDI-SLATE from a complete undergraduate biomedical engineering medical imaging course.
  • Collected 1117 high-resolution slides and paired them with narration refined via automatic speech recognition and manual cleanup.
  • Included lecture-level difficulty tags, key ideas, common student misunderstandings, and practice questions.

Main Results:

  • MEDI-SLATE contains 1117 slide-narration pairs covering diverse medical imaging topics.
  • The dataset includes metadata such as difficulty tags and common student misconceptions.
  • A reproducible preprocessing pipeline for data extraction, refinement, and alignment is provided.

Conclusions:

  • MEDI-SLATE is a high-fidelity, openly available resource for medical imaging education.
  • The dataset supports curriculum development, multimodal learning research, and AI tool creation.
  • All data and code are released for transparent use and future extensions.