— Marina Zimmermann, PhD
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.
Assistant Professor
since 2023 | Assistant Professor for Computational Pathology, Institute of Medical Systems Biology (IMSB) |
2011 - 2013
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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 |
2014 - 2018- 2020
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PhD in Electrical Engineering, Signal Processing Laboratory 5, Ecole polytechnique fédérale de Lausanne (EPFL), Switzerland, Supervisor: Prof. Jean-Philippe Thiran |
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
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Postdoctoral researcher, Institute of Medical Systems Biology (IMSB) and III. Department of Medicine, University Medical Center Hamburg-Eppendorf (UKE), Germany |
2013 - 2018
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Research assistant, Signal Processing Laboratory 5, Ecole polytechnique fédérale de Lausanne (EPFL), Switzerland |
1. | 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. | 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. | 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. |
5. | 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. |
* Represents equal contributions as co-first or co-senior authors. |
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