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Introduction: MRI and CT scans are crucial advancements in medical imaging techniques, playing a vital role in diagnosing conditions related to the gastrointestinal (GI) system. Each scan serves distinct purposes, targets specific areas, and requires unique nursing duties.
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Computed Tomography (CT) scan:
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Imaging Studies III: Computed Tomography

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Imaging Studies for Cardiovascular System V: CT

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Cardiovascular magnetic resonance imaging, or CMRI, is a non-invasive diagnostic test that employs a magnetic field and radiofrequency waves to create precise images of the heart and arteries. It provides comprehensive information about cardiac anatomy, function, perfusion, and tissue characterization without ionizing radiation.IndicationsCMRI diagnoses various heart conditions, including tissue damage from heart attacks, ischemic heart disease, myocarditis, aortic issues (tears, aneurysms,...

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Related Experiment Video

Updated: Apr 18, 2026

Author Spotlight: An Efficient and Robust Software for Automated Fusion of Multiple Preclinical Imaging Modalities

Author Spotlight: An Efficient and Robust Software for Automated Fusion of Multiple Preclinical Imaging Modalities

Published on: October 27, 2023

MILU: a consensus ensemble benchmark for multimodal medical imaging lecture understanding.

Md Motaleb Hossen Manik¹, Md Zabirul Islam¹, Ge Wang²

¹Rensselaer Polytechnic Institute, Department of Computer Science, Troy, New York, United States.

Journal of Medical Imaging (Bellingham, Wash.)

|April 17, 2026

Summary

Vision-language models (VLMs) show high formatting reliability but low semantic consistency on scientific lecture slides. The Medical Imaging Lecture Understanding (MILU) benchmark reveals significant variability in their structured understanding.

Keywords:

consensus ensemble lecture-level multimodal reasoning medical imaging education multimodal benchmark dataset multimodal lecture slides vision–language models

Related Experiment Videos

Last Updated: Apr 18, 2026

Author Spotlight: An Efficient and Robust Software for Automated Fusion of Multiple Preclinical Imaging Modalities

Author Spotlight: An Efficient and Robust Software for Automated Fusion of Multiple Preclinical Imaging Modalities

Published on: October 27, 2023

Area of Science:

Artificial Intelligence
Computer Vision
Medical Education

Background:

Vision-language models (VLMs) are increasingly utilized for interpreting multimodal educational content.
The reliability of VLMs on complex scientific lecture slides with diagrams, equations, and dense text is not well-established.

Purpose of the Study:

Introduce the Medical Imaging Lecture Understanding (MILU) benchmark, a large-scale dataset for evaluating VLM performance on medical imaging lectures.
Characterize cross-model variability in the structured understanding of scientific lecture slides by VLMs.

Main Methods:

Evaluated four prominent VLMs (LLaVA-OneVision, InternVL3-14B, Qwen2-VL-7B, Qwen3-VL-4B) using unified prompts to generate structured JSON outputs.
Assessed parsing coverage, pairwise semantic agreement, lecture-level patterns, and alignment with a consensus ensemble.

Main Results:

All evaluated VLMs demonstrated high JSON formatting coverage (92%-99%) but exhibited extremely low semantic agreement (pairwise Jaccard indices 0.03-0.09).
Lecture-level stability varied, with mathematically structured content showing higher consistency than diagram-heavy slides.
A consensus ensemble showed modest alignment with individual models, highlighting both areas of convergence and systematic disagreement.

Conclusions:

MILU serves as the inaugural benchmark for assessing structured understanding of scientific lecture slides.
Current VLMs excel in formatting but lack semantic consistency, indicating a need for improved scientific lecture interpretation methods.
This benchmark lays the groundwork for future expert-annotated datasets, diagram/math-aware VLM development, and enhanced scientific content analysis.