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

While medical imaging generates vast amounts of information, only a fraction is currently used to inform clinical decisions. We bridge this gap by developing advanced algorithms and strategies that make this wealth of data accessible and actionable 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 Klinikum, we prioritize research on two key neurological diseases: Multiple Sclerosis and Brain Tumors.

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 leading challenges like BraTS and workshops such as BrainLes.

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