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Yale-BIDS-Chen/
medpmc-screening-pubmedbert-caption-reference

Name: medpmc-screening-pubmedbert-caption-reference
Availability: InStock

safetensors

bert

text-classification

medical-multimodal-data-curation

medpmc

2607.07673

microsoft/BiomedNLP-BiomedBERT-base-uncased-abstract-fulltext

finetune

cc-by-nc-sa-4.0

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MedPMC Initial Screening Model

This model is used in the initial screening stage of the MedPMC data curation pipeline. It is a text-based classifier that takes a figure caption and its inline reference text from a PubMed Central article as input and predicts whether the corresponding figure is likely to be a clinically relevant medical image for downstream multimodal data curation.

The model is initialized from microsoft/BiomedNLP-BiomedBERT-base-uncased-abstract-fulltext.

Task

The model performs binary classification.

Label	Meaning
0	Non-medical
1	Medical

Input format

This repository corresponds to the caption + reference text version of the initial screening model. The input text should concatenate the figure caption and inline reference text using the following format:

text

"Caption": {figure_caption}
"Reference Text": {reference_text_1}
{reference_text_2}
...

Quick start

python

import torch
from transformers import AutoTokenizer, AutoModelForSequenceClassification

repo_id = "Yale-BIDS-Chen/medpmc-screening-pubmedbert-caption-reference"

tokenizer = AutoTokenizer.from_pretrained(repo_id)
model = AutoModelForSequenceClassification.from_pretrained(repo_id)
model.eval()

caption = "Axial CT image showing a pulmonary nodule in the right upper lobe."
references = [
    "The CT findings demonstrated a solitary pulmonary nodule.",
    "Follow-up imaging was recommended."
]

text = '"Caption": ' + caption + '\n"Reference Text": ' + "\n".join(references)

inputs = tokenizer(
    text,
    return_tensors="pt",
    truncation=True,
    padding=True,
    max_length=512,
)

with torch.no_grad():
    outputs = model(**inputs)
    probs = torch.softmax(outputs.logits, dim=-1)
    pred = torch.argmax(probs, dim=-1).item()

print("Prediction:", pred)
print("Probabilities:", probs.tolist())

Model Performance

MedPMC includes multiple initial screening variants depending on the input text and model backbone. The table below summarizes the performance of different screening models evaluated on the MedPMC validation set.

Model	Input	Precision	Recall	F1
Keyword match	Caption + inline text; Keywords	70.6	62.2	61.7
Bioformer-16L	Caption only	92.2	92.3	92.2
Bioformer-16L	Caption + inline text	93.0	92.9	92.9
Bioformer-8L	Caption only	92.3	92.3	92.3
Bioformer-8L	Caption + inline text	92.3	92.6	92.5
BioLinkBERT-base	Caption only	93.0	92.7	92.9
BioLinkBERT-base	Caption + inline text	92.9	93.0	92.9
PubMedBERT-fulltext	Caption only	92.7	93.1	92.9
PubMedBERT-fulltext ⭐ (This model)	Caption + inline text	93.3	93.1	93.2

License

The model is released for non-commercial research use under CC BY-NC-SA 4.0.

Citation

bibtex

@article{kim2026medpmc,
  title={MedPMC: A Systematic Framework for Scaling High-Fidelity Medical Multimodal Data for Foundation Models},
  author={Kim, Hyunjae and Kim, Dain and Xiao, Pan and Applebaum, Serina S and Chung, Younjoon and Ai, Xuguang and Yin, Yu and Jiang, Roy and Du, Yuexi and Wei, Yawen and others},
  journal={arXiv preprint arXiv:2607.07673},
  year={2026}
}

Questions?

For questions or feedback, please contact Hyunjae Kim at hyunjae.kim@yale.edu.