Note

This is a community plugin, an external project maintained by its respective author. Community plugins are not part of FiftyOne core and may change independently. Please review each plugin’s documentation and license before use.

MedSigLIP in FiftyOne

MedSigLIP is a large-scale medical vision-language model developed by Google Health. It is designed to encode medical images and associated text into a shared embedding space, enabling advanced applications in healthcare AI.

This repository provides a FiftyOne integration for Google’s MedSigLIP embedding models, enabling powerful text-image similarity search capabilities in your FiftyOne datasets.

ℹ Important! Be sure to request access to the model!

This is a gated model, so you will need to fill out the form on the model card: https://huggingface.co/google/medsiglip-448

Approval should be instantaneous.

You’ll also have to set your Hugging Face in your enviornment:

export HF_TOKEN="your_token"

Or sign-in to Hugging Face via the CLI:

huggingface-cli login

About the model

• Architecture: Two-tower encoder, each with 400 million parameters: one for images (vision transformer) and one for text (text transformer).

• Input Support:

  • Images: 448x448 resolution

  • Text: Up to 64 tokens

• Training Data: Trained on a diverse mix of de-identified medical images and text pairs (e.g., chest X-rays, dermatology, ophthalmology, pathology, CT/MRI slices) plus natural image-text pairs.

• Primary Use Cases:

  • Medical image interpretation

  • Data-efficient and zero-shot classification

  • Semantic image retrieval

• Performance: Demonstrates strong zero-shot and linear probe performance across multiple medical imaging domains, outperforming or matching specialized models on key benchmarks.

• Recommended For: Healthcare AI developers seeking robust, general-purpose medical image and text embeddings, especially for classification and retrieval tasks (not for text generation).

Example Applications

• Zero-shot classification of medical images

• Semantic search in medical image databases

• Embedding generation for downstream machine learning tasks

Usage

Installation

Install the requirements: pip install fiftyone huggingface-hub accelerate sentencepiece protobuf

Download a sample dataset

You can use the SLAKE dataset as a running example. This is how to download it from the Hugging Face hub:

import fiftyone as fo

from fiftyone.utils.huggingface import load_from_hub

dataset = load_from_hub(
    "Voxel51/SLAKE",
    name="SLAKE",
    overwrite=True,
    max_samples=10
    )

Next, you need to register and download the model:

import fiftyone.zoo as foz

# Register this custom model source
foz.register_zoo_model_source("https://github.com/harpreetsahota204/medsiglip")

# Download your preferred SigLIP2 variant
# Note that you will need to acknowledge the license if you haven't yet of MedSiglip on HuggingFace if you haven't yet
foz.download_zoo_model(
    "https://github.com/harpreetsahota204/medsiglip",
    model_name="google/medsiglip-448",
)

Loading the Model

import fiftyone.zoo as foz

model = foz.load_zoo_model(
    "google/medsiglip-448"
)

Computing Image Embeddings

dataset.compute_embeddings(
    model=model,
    embeddings_field="medsiglip_embeddings",
)

Visualizing Embeddings

import fiftyone.brain as fob

results = fob.compute_visualization(
    dataset,
    embeddings="medsiglip_embeddings",
    method="umap",
    brain_key="medsiglip_viz",
    num_dims=2,
)

# View in the App
session = fo.launch_app(dataset)

Text-Image Similarity Search

import fiftyone.brain as fob

# Build a similarity index
text_img_index = fob.compute_similarity(
    dataset,
    model=m"google/medsiglip-448",
    brain_key="medsiglip_similarity",
    embeddings="medsiglip_embeddings"
)

# Search by text query
similar_images = text_img_index.sort_by_similarity("a photo of a chest x-ray")

# View results
session = fo.launch_app(similar_images)

License

This model is released with Health AI Developer Foundations Terms of Use. Refer to the official license for details.

Citation

@article{sellergren2025medgemma,
  title={MedGemma Technical Report},
  author={Sellergren, Andrew and Kazemzadeh, Sahar and Jaroensri, Tiam and Kiraly, Atilla and Traverse, Madeleine and Kohlberger, Timo and Xu, Shawn and Jamil, Fayaz and Hughes, Cían and Lau, Charles and others},
  journal={arXiv preprint arXiv:2507.05201},
  year={2025}
}