HCKH - PI Marina Zimmermann - Hamburg Center for Kidney Health

Group of Computational Pathology

Marina Zimmermann

Marina Zimmermann

Mission Statement

“Improve the understanding of diseases and their diagnosis through the automated analysis and quantification of microscopy images”

— Marina Zimmermann, PhD

Team Members

PhD student

Nico Kaiser

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Research

The Group of Computational Pathology develops algorithms to analyse medical images, in particular microscopy images, in order to improve clinical diagnoses and advance the understanding of diseases. One of the focuses of our work is the quantitative analysis and discovery of signatures of diseases based on the segmentation of microscopy images with deep learning and classical image processing techniques. In order to achieve these goals, we collaborate closely with other groups at the HCKH and the UKE.

Marina Zimmermann, PhD

Assistant Professor

Center for Molecular Neurobiology (ZMNH), University Medical Center Hamburg-Eppendorf (UKE)

Falkenried 94

20251 Hamburg, Germany

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CV

Current Position

since 2023

Assistant Professor for Computational Pathology, Institute of Medical Systems Biology (IMSB)

University Training

2011 - 2013	M.Sc. in Electrical and Electronics Engineering, Ecole polytechnique fédérale de Lausanne (EPFL), Switzerland
2008 - 2011	B.Sc. in Electrical and Computer Engineering, Jacobs University Bremen, Germany

Academic qualifications

2014 - 2018- 2020

PhD in Electrical Engineering, Signal Processing Laboratory 5, Ecole polytechnique fédérale de Lausanne (EPFL), Switzerland, Supervisor: Prof. Jean-Philippe Thiran

Previous professional career

Scientific postgraduate education:

2021 - 2023	Team lead, Computational Pathology at Institute of Medical Systems Biology (IMSB), University Medical Center Hamburg-Eppendorf (UKE), Germany
2018 - 2023	Postdoctoral researcher, Institute of Medical Systems Biology (IMSB) and III. Department of Medicine, University Medical Center Hamburg-Eppendorf (UKE), Germany
2013 - 2018	Research assistant, Signal Processing Laboratory 5, Ecole polytechnique fédérale de Lausanne (EPFL), Switzerland

Selected publications

1.	Robust, credible, and interpretable AI-based histopathological prostate cancer grading. Westhaeusser F, Fuhlert P, Dietrich E, Lennartz M, Khatri R, Kaiser N, Röbeck P, Bülow R, von Stillfried S, Witte A, Ladjevardi S, Drotte A, Severgardh P, Baumbach J, Puelles VG, Häggman M, Brehler M, Boor P, Walhagen P, Dragomir A, Busch C, Graefen M, Bengtsson E, Sauter G, Zimmermann M, Bonn S. medRxiv [Preprint]. 2024 Jul 10:2024.07.09.24310082.
2.	DeePSC: A Deep Learning Model for Automated Diagnosis of Primary Sclerosing Cholangitis at Two-dimensional MR Cholangiopancreatography. Ragab H, Westhaeusser F, Ernst A, Yamamura J, Fuhlert P, Zimmermann M, Sauerbeck J, Shenas F, Özden C, Weidmann A, Adam G, Bonn S, Schramm C. Radiol Artif Intell. 2023 Apr 19;5(3):e220160.
3.	Expansion-enhanced super-resolution radial fluctuations enable nanoscale molecular profiling of pathology specimens. Kylies D, Zimmermann M, Haas F, Schwerk M, Kuehl M, Brehler M, Czogalla J, Hernandez L, Konczalla L, Okabayashi Y, Menzel J, Edenhofer I, Mezher S, Aypek H, Dumoulin B, Wu H , Hofmann S, Kretz O, Wanner N, Tomas N, Krasemann S, Glatzel M, Kuppe C, Kramann R, Benjamin B, Schneider R, Urbschat C, Arck P, Gagliani N, Wiech T, Grahammer F, Saez P, Wong MN, Bonn S, Huber TB, Puelles, VG. Nat Nanotechnol. 2023 Apr;18(4):336-342.
4.	Deep learning-based molecular morphometrics for kidney biopsies. Zimmermann M, Klaus M, Wong MN, Thebille AK, Gernhold L, Kuppe C, Halder M, Kranz J, Wanner N, Braun F, Wulf S, Wiech T, Panzer U, Krebs CF, Hoxha E, Kramann R, Huber TB, Bonn S, Puelles VG. JCI Insight.* 2021 Apr 8;6(7):e144779.
5.	Deep Learning-Based Bias Transfer for Overcoming Laboratory Differences of Microscopic Images. Thebille AK, Dietrich E, Klaus M, Gernhold L, Lennartz M, Kuppe C, Kramann R, Huber TB, Sauter G, Puelles VG, Zimmermann M, Bonn S. Annual Conference on Medical Image Understanding and Analysis 2021.
6.	Towards Explainable End-to-End Prostate Cancer Relapse Prediction from H&E Images Combining Self-Attention Multiple Instance Learning with a Recurrent Neural Network. Dietrich E, Fuhlert P, Ernst A, Sauter G, Lennartz M, Stiehl HS, Zimmermann M, Bonn S. Machine Learning for Health 2021.
	* Represents equal contributions as co-first or co-senior authors.