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OpenMed/multicare-case-images

MultiCaRe: Open-Source Clinical Case Dataset MultiCaRe is an open-source, multimodal clinical case dataset built from the PubMed Central Open Access (OA) Case Report articles. It aggregates de-identified, open-access case narratives, figure images, captions, and rich article metadata across diverse specialties. Figures are mapped to their case text with explicit references and to article-level metadata, enabling grounded multimodal use. Source and process: OA case reports were… See the full description on the dataset page: https://huggingface.co/datasets/OpenMed/multicare-case-images.

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MultiCaRe: Open-Source Clinical Case Dataset

MultiCaRe is an open-source, multimodal clinical case dataset built from the PubMed Central Open Access (OA) Case Report articles. It aggregates de-identified, open-access case narratives, figure images, captions, and rich article metadata across diverse specialties. Figures are mapped to their case text with explicit references and to article-level metadata, enabling grounded multimodal use.

  • —Source and process: OA case reports were collected from PMC; article metadata and abstracts were parsed; figures were downloaded and split into subimages when needed; captions were aligned; and image labels were curated from a hierarchical medical taxonomy (>140 classes).
  • —Scale: 85k+ OA case reports, 160k+ images/subimages (v2.0).
  • —Tasks enabled: image-text retrieval, caption grounding, VQA/doc-QA, image classification, multimodal modeling.
  • —Citation: DATA journal paper — https://www.mdpi.com/2306-5729/10/8/123; Zenodo — https://zenodo.org/records/13936721.

This repository: one representative image per figure One row per figure (image_id) with a representative processed image and textual context that mentions the figure in the case narrative.

Highlights

  • —Representative image chosen per figure (preference: undivided > 'a' > first available).
  • —Includes text_references snippets to ground the figure in the case text.
  • —Join to image-, case-, and article-level datasets using stable keys.

Schema

  • —image: datasets.Image (PIL-compatible)
  • —image_id: original figure identifier (groups subimages)
  • —file: processed image filename used as the representative
  • —caption: figure caption (original)
  • —text_references: newline-joined excerpts that mention this figure
  • —tag: PubMed file tag
  • —caseid: equals cases.caseid
  • —article_id: PMCID
  • —fileid, patientid, license: cross-links and license string

Quick start

python
from datasets import load_dataset
ds = load_dataset("openmed-community/multicare-case-images", split="train")

row = ds[0]
row["image"].show()
print(row["caption"])          # caption of the figure
print(row["text_references"])  # where it appears in the case text

Joins

  • —imageid ↔ images.mainimage
  • —caseid ↔ cases.caseid
  • —articleid ↔ articles.articleid

Notes

  • —Prefer patient/article-level splits downstream.
  • —Per-item OA licenses are preserved.