AI for Image-Guided Diagnosis and Therapy
Prof. Dr. Benedikt Wiestler

Medical imaging produces vast amounts of data, yet only a small fraction is currently utilized in clinical decision-making. We bridge this gap by developing advanced algorithms and strategies that transform this wealth of information into accessible and actionable insights for clinicians.

To achieve this goal, we are focusing on three main research areas:

• Learning efficient, (self)supervised representations (and embeddings) for medical (imaging) data

• Developing predictive, in part bio-physically informed, models for disease assessment and individualized therapy

• Designing algorithms and models that leverage the rich, synergistic information from multimodal medical data

Our lab is a joint appointment between TUM Radiation Oncology and TUM Neuroradiology. Together with our scientific and clinical partners at TUM and TUM Universitätsklinikum, we prioritize research on two key neurological diseases: Multiple Sclerosis and Brain Tumors.

Within the vibrant Munich AI ecosystem, our lab is part of the MCML.

Committed to open science, we strive to make all of our developed tools available publicly. We further contribute to the advancement of the field by actively participating in and co-organizing leading challenges like BraTS or ISLES and workshops such as BrainLes.

We are supported by the SFB-824, Deutsche Krebshilfe, TUM-KKF, ZD.B, BMBF, DFG and NIH.