MedSigLIP is a variant of SigLIP(Sigmoid Loss for Language Image Pre-training) that is trained to encode medical images and text into a common embedding space. Developers can use MedSigLIP to accelerate building healthcare-based AI applications. MedSigLIP contains a 400M parameter vision encoder and 400M parameter text encoder, it supports 448x448 image resolution with up to 64 text tokens.
MedSigLIP is recommended for medical image interpretation applications without a need for text generation, such as data-efficient classification, zero-shot classification, and semantic image retrieval. For medical applications that require text generation, MedGemma is recommended.
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Read our developer documentation to see the full range of next steps available, including learning more about the model through its model card.
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Explore this repository, which contains notebooks for using the model from Hugging Face and Vertex AI as well as the implementation of the container that you can deploy to Vertex AI.
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Visit the model on Hugging Face or Model Garden.
We are open to bug reports, pull requests (PR), and other contributions. See CONTRIBUTING and community guidelines for details.
While the model is licensed under the Health AI Developer Foundations License, everything in this repository is licensed under the Apache 2.0 license, see LICENSE.