---
license: cc-by-3.0
task_categories:
- zero-shot-classification
language:
- en
pretty_name: LitMedImage
size_categories:
- 10K<n<100K
---

Dataset Name: LitMedImage – literature-derived Medical vs. Non-Medical Image Dataset

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Description:
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LitMedImage is a curated dataset of biomedical literature figures labeled as MEDICAL or NON-MEDICAL. The dataset is built from images extracted from PubMed Central Open Access (PMC-OA) articles and includes corresponding captions and parsed image metadata. Labels were generated using a large language model (LLM) following strict imaging definitions. This dataset is intended for research in figure classification, document parsing, and biomedical vision-language models.

Columns:

PMCID: PubMed Central article identifier (e.g., PMC1234567)
Image_num: Index of the image within the article
Online_file_path: Direct file path to the image under https://ftp.ncbi.nlm.nih.gov/pub/pmc/

Image_info_Cleaned: Parsed metadata describing the image contents
Caption_Clean: Cleaned image caption from the original publication
label: Binary classification label ("yes" for MEDICAL images, "no" for NON-MEDICAL images)

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Label Definitions:
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Label = "yes" (MEDICAL)
Images that belong to clinical or biomedical imaging modalities, including:
Radiology, Echocardiography, Dermoscopy, Histopathology, X-ray (Radiography), CT, MRI, Ultrasound, Nuclear Medicine (PET, SPECT, PET-CT, PET-MRI), Optical Imaging, Thermography, Elastography, Mammography, Digital Breast Tomosynthesis, Fluoroscopy, Clinical Imaging

Label = "no" (NON-MEDICAL)
Images that are statistical plots or schematic illustrations, including:
Bar charts, Histograms, Line graphs, Scatter or Bubble plots, Pie or Donut charts, Area charts, Heatmaps, Box or Violin plots, Radar or Spider charts, Treemaps, Network graphs, Drawings, Conceptual diagrams

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Task Instruction:
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Given an image, classify whether it is a MEDICAL image or a NON-MEDICAL image.

Data Source:
All images originate from the PubMed Central Open Access Subset via the public FTP archive at:
https://ftp.ncbi.nlm.nih.gov/pub/pmc/

Intended Use:

- Binary image classification (medical vs. non-medical)
- Multimodal image + caption classification
- Figure filtering for automated document processing
- Pre-filtering figures for vision-language model training or inference

Citation:
[To be added]