Register MedGemma, MedSigLIP Presets to kerashub

[laxmareddyp]

Description of the change

Reference

Colab Notebook

Checklist

• I have added all the necessary unit tests for my change.
• I have verified that my change does not break existing code and works with all backends (TensorFlow, JAX, and PyTorch).
• My PR is based on the latest changes of the main branch (if unsure, rebase the code).
• I have followed the Keras Hub Model contribution guidelines in making these changes.
• I have followed the Keras Hub API design guidelines in making these changes.
• I have signed the Contributor License Agreement.

[gemini-code-assist]

Summary of Changes

Hello @laxmareddyp, I'm Gemini Code Assist¹! I'm currently reviewing this pull request and will post my feedback shortly. In the meantime, here's a summary to help you and other reviewers quickly get up to speed!

This pull request significantly expands the Keras Hub's offerings by integrating specialized medical AI models. It introduces MedGemma models for advanced medical text generation and comprehension, and MedSigLIP for multimodal medical image and text understanding. These additions provide developers with powerful, pre-trained assets tailored for various medical AI applications, enhancing the platform's utility in the healthcare domain.

Highlights

• New MedGemma Model Presets: Three new MedGemma instruction-tuned model presets (4B, 27B, and 27B text-only variants) have been registered. These models are built on Gemma 3 and are specifically trained for medical text and image comprehension, optimized for medical text generation applications.
• New MedSigLIP Model Preset: A new MedSigLIP 900M parameter model preset has been registered. This variant of SigLIP is designed to encode medical images and text into a shared embedding space, featuring a 400M vision encoder and a 400M text encoder, supporting 448x448 image resolution.

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[gemini-code-assist]

Code Review

This pull request adds new presets for MedGemma and MedSigLIP models. My review focuses on improving the clarity and accuracy of the model descriptions in the preset files. Specifically, I've suggested corrections for grammatical errors, formatting, and a misleading description for a text-only MedGemma model. I've also pointed out an inconsistency in the parameter count for the MedSigLIP model description.