(2019)
Künstliche Intelligenz für die biomedizinische Bildgebung.
GIT Labor-Fachzeitschrift ,
(Review)
Carl-Magnus Svensson
Applied Systems Biology · DropCode +49 3641 532-1088 carl-magnus.svensson@leibniz-hki.deCurriculum vitae
Main Research Areas
- signal and data analysis
- analysis of medical and microscopy images using computer vision approaches
- statistical modeling and machine learning
Professional Career
since 2012 | Postdoctoral researcher in the Applied Systems Biology group, Hans Knöll Institute, Jena, Germany. |
2011-2012 | Postdoctoral researcher in the group Computational Neuroscience and Machine Learning at Frankfurt Institute for Advanced Studies (FIAS) in Frankfurt am Main |
2009-2011 | Postdoctoral researcher in the Visual Neuroscience group at the School of Psychology at the University of Nottingham (GB) |
2009 | PhD in Applied Mathematics at the University of Nottingham (GB) |
2005 | M.Sc. in Engineering Physics at the Royal Institute of Technology in Stockholm (SWE) |
Publications
(2019)
Coding of experimental conditions in microfluidic droplet assays using colored beads and machine learning supported image analysis.
Small 15(4),
e1802384.
(2019)
Clinical S. aureus isolates vary in their virulence to promote adaptation to the host.
Toxins 11(3),
135.
(2018)
Candida albicans-induced epithelial damage mediates translocation through intestinal barriers.
mBio 9(3),
e00915.
(2018)
Untangling cell tracks: Quantifying cell migration by time lapse image data analysis.
Cytometry A 93(3),
357-370.
(Review)
(2017)
Mast cells acquire MHCII from dendritic cells during skin inflammation.
J Exp Med 214(12),
3791-3811.
(2017)
Segmentation of clusters by template rotation expectation maximization.
Comput Vis Image Underst 152,
64-72.
(2017)
Quantification of arthritic bone degradation by analysis of 3D micro-computed tomography data.
Sci Rep 7,
44434.
(2015)
Estimation of cortical magnification from positional error in normally sighted and amblyopic subjects.
Journal of Vision 15(2),
1-16.
(2015)
Automated quantification of the phagocytosis of Aspergillus fumigatus conidia by a novel image analysis algorithm.
Frontiers in Microbiology 6(549),
*authors contributed equally.