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🔗 References¶

This page is a starting point if you want to read the underlying work or explore models and tools linked to the challenge. Publications points to papers and preprints; Models and resources points to weights, APIs, and other pages you can try or build on.

Publications¶

Journal articles and preprints. Each entry lists a primary venue link (and a secondary link where helpful).

  1. Radford et al. (2021). Learning transferable visual models from natural language supervision. Proceedings of the 38th International Conference on Machine Learning (ICML), PMLR 139, 8748–8763.

    Links: PMLR proceedings · arXiv

  2. Saab et al. (2024). Capabilities of Gemini models in medicine. arXiv:2404.18416.

    Links: arXiv

  3. Xu et al. (2024). A whole-slide foundation model for digital pathology from real-world data (introduces Prov-GigaPath). Nature 630, 181–188. https://doi.org/10.1038/s41586-024-07441-w

    Links: Nature · DOI

  4. Shaikovski et al. (2024). PRISM: A Multi-Modal Generative Foundation Model for Slide-Level Histopathology. arXiv:2405.10254.

    Links: arXiv

  5. Ding et al. (2025). A multimodal whole-slide foundation model for pathology (associated model: TITAN). Nature Medicine. https://doi.org/10.1038/s41591-025-03982-3

    Links: Nature Medicine · DOI

Models and resources¶

Named models, technical reports tied to a product line, and open weights—not substitutes for the publication list above when you are citing the underlying research.

  1. H-Optimus — Open foundation vision model for histopathology (Bioptimus). Associated description: Saillard, C., Jenatton, R., Llinares-López, F., Mariet, Z., Cahané, D., Durand, E., & Vert, J.-P. (2024). H-optimus-0.

    Links: Hugging Face: bioptimus/H-optimus-0 · GitHub releases (v0)

  2. MedGemma 1.5 — Open multimodal Gemma variant for medical text and imaging (Google). Technical report: Sellergren, A., et al. (2025). MedGemma Technical Report. arXiv:2507.05201.

    Links: arXiv · Model card (Google) · GitHub: google-health/medgemma

  3. OpenBioLLMs — Open-weight LLaMA 3–based language models for healthcare and life sciences. Project citation: Pal, M. S. A., & Sankarasubbu, M. (2024). OpenBioLLMs: Advancing open-source large language models for healthcare and life sciences.

    Links: Hugging Face: OpenBioLLM-Llama3-70B