About us

The Institute for Computational Imaging and AI in Medicine (CompAI) at TUM and the Institute of Machine Learning in Biomedical Imaging (IML) at Helmholtz Center Munich focus on research to leverage machine learning for the grand challenges in biomedical imaging in areas of unmet clinical need. Novel and affordable solutions should empower clinics to make more accurate, fast and reliable decisions for early detection, treatment planning and improved patient outcome. We are looking for team members, please contact us.

Meet the Team

Chair

Julia A. Schnabel

Technical University Munich

Helmholtz Center Munich

King's College London

Professor for Computational Imaging and AI in Medicine, Director of the Institute of Machine Learning in Biomedical Imaging

Biomedical Imaging, Artificial Intelligence in Medicine

Team Support

Sandra Mayer

Helmholtz Center Munich

Administrative Assistant

Project Management and Administration, Team Management and Support, Communication and Relations

Sabine Franke

Technical University Munich

Administrative Assistant

Project Management and Administration, Team Management and Support, Communication and Relations

Principal Investigators

Georgios Kaissis

Helmholtz Center Munich

Principal Investigator

Reliable artificial intelligence, Medical image computing, Probabilistic methods

Veronika Zimmer

Technical University Munich

Principal Investigator

Medical Image Computing, Ultrasound Image Analysis, Fetal Image Analysis

Senior Researchers

Emily Chan

Helmholtz Center Munich

King's College London

Research Scientist

Medical image computing, Transfer learning, Multi-modal learning

Maxime Di Folco

Helmholtz Center Munich

Research Scientist

Representation learning, Cardiac imaging

Daniel M. Lang

Helmholtz Center Munich

Research Scientist

Self-supervised Learning, Transfer Learning, Survival Analysis

Researchers

Sameer Ambekar

Technical University of Munich

Helmholtz Center Munich

PhD Student

Domain Generalization, Meta Learning, Variational Inference

Hannah Eichhorn

Technical University Munich

Helmholtz Center Munich

PhD student

Magnetic Resonance Imaging, Deep learning, Image reconstruction

Cosmin I. Bercea

Technical University of Munich

Helmholtz Center Munich

PhD Student

Generative AI, Unsupervised Representation Learning, Anomaly Detection

Stefan Fischer

Technical University of Munich

MRI TUM Munich

PhD Student

Interpretable Machine Learning, Generative Modelling, Radiooncology

Johannes Kiechle

Technical University of Munich

MRI TUM Munich

PhD Student

Shape Analysis, Representation Learning, Magnetic Resonance Imaging

Jun Li

Technical University of Munich

Helmholtz Center Munich

PhD Student

Vision and Language, Multi-Modal Learning, Cross-Modality Generation

Anna Reithmeir

Technical University of Munich

Munich Center for Machine Learning (MCML)

PhD Student

Medical Image Registration, Robustness and Numerics of Machine Learning Algorithms, Manifold Learning

Anneliese Riess

Technical University of Munich

Helmholtz Center Munich

PhD Student

Mathematical Foundations of Privacy Preserving AI, Probability Theory

Veronika Spieker

Technical University Munich

Helmholtz Center Munich

PhD Student

Machine Learning, Magnetic Resonance Imaging, Motion Detection & Correction

Alumni

Simona Bottani

Helmholtz Center Munich

Research Scientist

Deep learning, Big data sets, Brain imaging

News

Review paper accepted at IEEE Transactions on Medical Imaging

Deep Learning for Retrospective Motion Correction in MRI: A Comprehensive Review by Veronika Spieker and Hannah Eichhorn et al. has been accepted for publication at IEEE Transactions on Medical Imaging.

Veronika Spieker, Hannah Eichhorn

Oct 25, 2023 1 min read

Review paper accepted at IEEE Transactions on Medical Imaging

Five papers accepted at MICCAI 2023 workshops

Five papers have been accepted for publication at workshops associated with the 26th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2023, which will be held from October 8th to 12th 2023 in Vancouver, Canada.

Interested to hear more about our work? Then join us at the following workshops:

Hannah Eichhorn, Veronika Spieker, Cosmin I. Bercea, Daniel M. Lang, Maxime Di Folco

Sep 14, 2023 1 min read

Five papers accepted at MICCAI 2023 workshops

Two papers accepted at MICCAI 2023

“What Do AEs Learn? Challenging Common Assumptions in Unsupervised Anomaly Detection and Reversing the Abnormal: Pseudo-Healthy Generative Networks for Anomaly Detection by Cosmin I. Bercea et al. have been accepted for publication at the 26th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2023, which will be held from October 8th to 12th 2023 in Vancouver, Canada.

Cosmin I. Bercea

May 26, 2023 1 min read

Paper accepted at ICML IMLH 2023

We are delighted to announce that our research on developing automatic diffusion models for anomaly detection has been accepted and will be published in the proceedings of the 3rd workshop for Interpretable Machine Learning in Healthcare, held at the International Conference on Machine Learning 2023. Congratulations to our dedicated student Michael for his outstanding contribution to this achievement!

Cosmin I. Bercea

May 25, 2023 1 min read

Paper accepted at MIDL 2023 (oral talk)

“Generalizing Unsupervised Anomaly Detection: Towards Unbiased Pathology Screening” by Cosmin I. Bercea et al. has been accepted for publication at Medical Imaging with Deep Learning, Nashville, 2023. Cosmin Bercea will present his work on Monday, 10 July 2023 at 9:15 pm CEST.

Cosmin I. Bercea

Apr 28, 2023 1 min read