Maintainer: microsoft

Total Score


Last updated 5/17/2024


Model LinkView on HuggingFace
API SpecView on HuggingFace
Github LinkNo Github link provided
Paper LinkNo paper link provided

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Model Overview

BiomedCLIP-PubMedBERT_256-vit_base_patch16_224 is a biomedical vision-language foundation model developed by researchers at Microsoft. It was pre-trained on the PMC-15M dataset, a large collection of 15 million figure-caption pairs from biomedical research articles in PubMed Central, using contrastive learning. The model uses PubMedBERT as the text encoder and a Vision Transformer as the image encoder, with domain-specific adaptations.

Similar models include CLIP, a general-purpose vision-language model trained on a large web corpus, Bio_ClinicalBERT, a biomedical language model trained on clinical notes, and BioGPT, a generative biomedical language model.

Model Inputs and Outputs

BiomedCLIP-PubMedBERT_256-vit_base_patch16_224 takes two inputs: an image and a text caption. The model encodes the image using the Vision Transformer and the text using PubMedBERT, then computes the similarity between the two representations.


  • Image: A biomedical image
  • Text: A caption or description of the biomedical image


  • Similarity score: A scalar value representing the similarity between the image and text inputs


BiomedCLIP-PubMedBERT_256-vit_base_patch16_224 excels at a variety of biomedical vision-language tasks, including cross-modal retrieval, image classification, and visual question answering. The model establishes new state-of-the-art performance on several standard benchmarks in these areas, substantially outperforming prior approaches.

What Can I Use It For?

Researchers can use BiomedCLIP-PubMedBERT_256-vit_base_patch16_224 to build novel applications in the biomedical domain that combine vision and language understanding. Some potential use cases include:

  • Biomedical image search and retrieval
  • Automatic captioning of biomedical images
  • Visual question answering on medical topics
  • Multimodal analysis of biomedical literature

The model is intended for research purposes only and should not be deployed in any production systems. The maintainers caution that the model's generation capabilities are also not suitable for production use.

Things to Try

One interesting aspect of BiomedCLIP-PubMedBERT_256-vit_base_patch16_224 is its strong performance on cross-modal retrieval tasks. Researchers could experiment with using the model to find relevant biomedical images for a given text query, or vice versa. The model's ability to align visual and textual representations could also be leveraged for tasks like medical image captioning or visual question answering.

Another promising direction is to fine-tune the model on specialized downstream tasks, leveraging the rich pre-training on biomedical data. This could unlock new capabilities tailored to particular medical domains or applications.

This summary was produced with help from an AI and may contain inaccuracies - check out the links to read the original source documents!

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