Helmholtz Foundation Model Initiative (HFMI) Projects


The Helmholtz Foundation Model Initiative (HFMI) was launched in February 2024 to explore how foundation models – AI models trained on large and diverse datasets – can support a broad range of research applications. Coordinated by Helmholtz Imaging and Helmholtz AI, HFMI aims to leverage AI’s potential to improve scientific data analysis and interpretation.

The initiative funds seven pilot projects and a Synergy Unit for cross-cutting activities, with a budget of 11 million euros over three years, plus 12 million euros allocated to infrastructure development. The open-source models developed through HFMI will enable a broader research community to benefit from AI-driven advancements.

Helmholtz Imaging is involved in three of the funded projects: The Human Radiome Project, AqQua, and the Synergy Unit.

Helmholtz Foundation Model Initiative (HFMI) Projects – Project overview


Microcosmos of the Ocean by Klas Ove Möller, Hereon
Image: Klas Ove Möller, Hereon

AqQua

AqQua aims to build the first foundational pelagic imaging model using billions of aquatic images worldwide. These images, spanning species from plankton, will help an AI classify species, extract traits, and estimate carbon content, offering key insights into biodiversity, ecosystem health, and the biological carbon pump’s role in climate regulation.

decorative image
Image: NicoElNino on Shutterstock

Synergy Unit

The Synergy Unit amplifies the Helmholtz Foundation Model Initiative’s impact by developing AI principles for diverse fields. Collaborating with HFMI projects, it focuses on knowledge sharing, community building, and representation to ensure the initiative’s lasting influence.

Image: ChatGPT. Prompt: MRI image of the knee with a touch of AI

The Human Radiome Project (THRP)

The Human Radiome Project (THRP) aims to drive a paradigm shift in medical research, providing novel insights into human health and disease through the power of AI. By integrating diverse radiological data, it seeks to enable groundbreaking advancements in personalized medicine, enhancing diagnostic accuracy and improving patient care.