MedFM 1st Workshop on Medical Foundation Models: From Pretraining to Clinical Impact

Senior Scientist at the German Cancer Research Center (DKFZ) in Heidelberg and a board member of the Division of Medical Image Computing. From 2020 to 2025, he led the Helmholtz Imaging Applied Computer Vision Lab, where he coordinated the development of state-of-the-art AI methods for medical image analysis and their translation into clinical and research practice within the Helmholtz Association. He is best known as the creator of nnU-Net, a self-configuring deep learning framework that has become the de facto standard for medical image segmentation and is widely used across academia, industry, and clinical research. Dr. Isensee studied Molecular Biotechnology at Heidelberg University and obtained his PhD at DKFZ and Heidelberg University with a focus on automated design of segmentation methods for biomedical imaging. His work has been published in leading venues such as Nature Methods and The Lancet Oncology and has been recognized through numerous international challenge wins. In 2025, he was awarded the Leopoldina Prize for Junior Scientists for his contributions to AI driven medical image analysis. His current research focuses on interactive segmentation systems (nnInteractive) and the development of foundation models for 3D radiological imaging, with the aim of building robust, adaptable, and user centric AI tools that can generalize across tasks, anatomies, and clinical settings.