Projects
With our Helmholtz Imaging Projects, Helmholtz Foundation Model Initiative (HFMI) and third-party funded projects, we aim to initiate cross-cutting research collaborations and identify innovative research topics in the field of imaging and data science.
Helmholtz Imaging offers a funding line of Helmholtz Imaging Projects, striving to seed collaborations between centers and across research fields. They are a strong incentive to enable interdisciplinary collaboration across the Helmholtz Association and an incubator and accelerator of the Helmholtz Imaging network.
In addition to our Helmholtz Imaging Projects, the Helmholtz Imaging team has secured external funding for third-party projects contributing their knowledge and expertise on cutting-edge imaging methodology.
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Find out more about Helmholtz Imaging Project call in this summary.
Helmholtz Imaging Projects
BrainShapes
Laplace-Beltrami shape descriptors of brain structures: Comparative optimization and genetic dissection
The project explores the 3D structure of the human brain by creating a digital 'map' of the brain and examining its unique genetic properties, potentially linking genetic variations to brain disorders.
HIT Permafrost
The Hidden Image of Thawing Permafrost
The project aims to develop a method for determining just how extensively thaw processes have already progressed in permafrost regions. The machine learning approach to be developed will be used to analyse radar images from aircraft in order to learn more about the properties of the subsurface permafrost.
Fast-EMI
Deep-learning assisted fast in situ 4D electron microscope imaging
A novel imaging approach combining electron microscopy and deep learning has been established. This method enables adaptive tracking of atomic defects, accelerating material development for the energy transition.Helmholtz Foundation Model Initiative (HFMI) Projects
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.
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.
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.UNLOCK – Benchmarking Projects
AIMBIS – Artificial Intelligence for Microscopic Biodiversity Screening
Manual microscopic biodiversity monitoring is time-consuming and requires expert knowledge, limiting the potential for biodiversity monitoring, hence to recognize the impacts of climate and environmental change on crucial ecosystem functions.
TIMELY: Time-series Integration across Modalities for Evaluation of Latent DYnamics
TIMELY provides the first comprehensive benchmark for multimodal biological time-series data, addressing the lack of standardized, high-quality datasets for modeling complex dynamical systems. It fosters the development of statistical and foundation models tailored to the analytical needs of research in biomedicine and neuroscience.
ADD-ON: Adenylation Domain Database and Online Benchmarking Platform
ADD-ON addresses the lack of reliable data for predicting how microbial enzymes assemble peptide-based natural products. By enabling accurate AI-driven structure prediction, it accelerates the discovery of new bioactive compounds and ultimately supports efforts to combat antimicrobial resistance.Third-Party Projects
COMFORT
COMFORT aims to achieve breakthroughs in developing compact, flexible, and robust machine learning models for image, audio, and network data. In doing so, its application-oriented research program will advance the mathematical understanding of machine learning at the intersection of effectiveness and robustness.
Foundations of Supervised Deep Learning for Inverse Problems
Recently, deep learning methods have excelled at various data processing tasks including the solution of ill-posed inverse problems. The goal of this project is to contribute to the theoretical foundation for truly understanding deep networks as regularization techniques which can reestablish a continuous dependence of the solution on the data.