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Department of Quantitative Biomedicine

Bjoern Menze

Menze group picture

Menze group at Huberspitz Alm, Hausham, Bavaria (2021)

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Biomedical Image Analysis and Machine Learning

Since September 2020, Prof. Dr. Bjoern Menze is part of DQBM's professorial staff as Full Professor for Biomedical Image Analysis and Machine Learning, funded by the Helmut Horten Foundation

Our work is in biomedical image computing, exploring topics at the interface of machine learning, medical imaging, and image-based modeling. Very broadly, we are interested in developing computational methods for transforming the qualitative visual inspection of biomedical image data into a quantitative description of the image information and a functional interpretation of the underlying disease process. In this, we are using models from biophysics, computational physiology, and machine learning - focusing on applications in clinical neuroimaging and the modeling of tumor growth. We are also interested in how to apply such models to big data bases in order to learn about correlations between model features and disease patterns at a population scale.

Our technology contributes to a systematic testing of biomedical modeling approaches on real world image data and, hence, to improve the understanding of the image-marker generating processes underlying selected landmark applications. At the patient level, it adds to the design of patient-specific treatments in personalized medicine.



These funding agencies support the Menze group:

Funding bodies Menze group

Weiterführende Informationen

Bjoern Menze

Prof. Dr. Bjoern Menze, Professor for Biomedical Image Analysis and Machine Learning

Department of Quantitative Biomedicine
Schmelzbergstrasse 26, floor D
8006 Zürich

Phone: +41 44 635 27 24



Administrative Assistant:
Claudia Stenger
Schmelzbergstrasse 26, floor D
phone: +41 44 635 66 31


Mail address:

Universitätsspital Zürich
Biomedizinische Bildanalyse / Biomedical Image Analysis
[Name Recipient]
Rämistrasse 100
8091 Zürich

Twitter Feed

  • @menze_group: Happy to be part of the Dagstuhl-Seminar on Inverse Biophysical Modeling and Machine Learning in Personalized Oncology:

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  • @menze_group: Excited to share our latest MedIA work on how to solve the inverse problem for brain tumor modeling. Congratulations on the great work @i_ezho @UZH_en @DQBM_uzh

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  • @menze_group: Congratulations! @chinmayprabhak5 for your first oral at @GeoMedIA22 on the utility of knowledge graphs for zero-shot chest radiograph classification. Visit our poster at 1500 for a discussion including heterogeneous knowledge graphs. @UZH_en @DQBM_uzh

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  • @menze_group: Excited to see an application from previous work

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  • @menze_group: Congrats @tommykn0cker for the best paper MIDL 1st runner-up featuring in Computer Vision News. Full read on our approach for shape reconstruction here: @DQBM_uzh @UZH_ch

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