IDP/Thesis: Physics-based machine learning for hyperspectral neuronavigation

Physics-based machine learning for hyperspectral neuronavigation

20.03.2025, Diplomarbeiten, Bachelor- und Masterarbeiten

At the lab for AI in Medicine, we offer a project on physics-based machine learning for hyperspectral neuronavigation

Hyperspectral imaging (HSI) is an optical technique that processes the electromagnetic spectrum at a multitude of monochromatic, adjacent frequency bands. The wide-bandwidth spectral signature of a target object’s reflectance allows fingerprinting its physical, biochemical, and physiological properties. HSI has been applied for various applications, such as remote sensing and biological tissue analysis. Recently, HSI was also used to differentiate between healthy and pathological tissue under operative conditions in a surgery room on patients diagnosed with brain tumors [1]. Within the HyperProbe project, we aim to develop a novel all-optical, AI-powered intraoperative imaging system to transform monitoring of brain tumour surgery. Your goal would be to develop a methodology at the intersection between physics and machine-learning to identify biomarkers of healthy and tumor brain tissue.

Your qualifications:

• Enthusiasm for combining physics and machine learning to develop new biomedical imaging modality
• Experience with image processing
• Strong programming skills in Python
• Background in Physics, Informatics, Computational Science and Engineering, or a related field

What we offer:

• An exciting research project aimed to build a new imaging modality that has the potential to transform neurosurgery monitoring in the near future
• Close supervision and access to state-of-the-art computer hardware
• The chance to work in a team of experts in image processing, deep learning, biomedical engineering, and medicine

How to apply: Send an email to ivan.ezhov@tum.de with your CV and transcript.

Kontakt: ivan.ezhov@tum.de