BIFOLD researcher Dr. Mina Jamshidi Idaji to lead a € 1.5 million research consortium on AI in Oncology
A consortium, led by BIFOLD/TU Berlin together with LMU Munich and UKE Hamburg, brings together oncologists, pathologists, and machine learning researchers to build AI models that integrate histopathological images (i.e., digital images of tissue samples) with molecular measurements and clinical information. Funded with € 1.5 million over 3 years, the project starts on October 1, 2026.
When oncologists assess a cancer patient, they draw on different types of information, including findings from histopathological examination, molecular analyses of the tumor, laboratory results, and other clinical data. Many AI systems examine these data sources separately, while existing multimodal models often treat all available information as a fixed snapshot of the patient.
In clinical practice, however, the picture of an individual patient develops as new findings become available. A new research consortium aims to reflect this dynamic process by developing AI systems that integrate different data sources and adapt to the information available at each stage of a patient's diagnostic journey.
For patients, this could mean that oncologists receive AI-assisted assessments that make better use of the evolving picture of their disease. Several further research and validation steps will be necessary before such an approach can be considered for clinical use.
"I am particularly excited that the funding will allow us to pilot our developed system in a real-world clinical setting," says Dr. Mina Jamshidi Idaji, a postdoctoral researcher in the Machine Learning Group at BIFOLD and TU Berlin, who leads the research consortium. "Our consortium benefits greatly from the involvement of clinicians who work directly in oncological diagnostics and bring extensive medical experience to the project."
The grant was awarded under the "Zukunft eHealth" junior-consortium funding line of the German Federal Ministry of Research, Technology and Space (BMFTR). The project starts on October 1, 2026, and will run for three years. The consortium further includes Prof. Dr. Philipp Jurmeister of Ludwig Maximilian University of Munich (LMU) and Prof. Dr. Michael Bockmayr of University Medical Center Hamburg-Eppendorf (UKE).
About Zukunft eHealth
The "Zukunft eHealth" junior-consortium funding line aims to advance digital innovation in health research and strengthen early-career scientists' independence. It pursues two goals. First, developing computational methods such as AI, simulations, and data-driven models to improve biomedical insights and patient predictions. Second, it allows outstanding postdoctoral researchers to independently lead interdisciplinary research consortia and establish themselves within the German research landscape.
About Dr. Mina Jamshidi Idaji
Dr. Mina Jamshidi Idaji is currently a postdoctoral researcher at the Machine Learning Group of BIFOLD, supervised by BIFOLD´s co-director Prof. Klaus-Robert Müller. Her work focuses on machine learning methods for healthcare applications, including computational pathology, ophthalmology, and biomedical signal processing, with an emphasis on interpretable and robust AI pipelines.
