Florian Goldmann - Pattern Recognition Lab

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Florian Goldmann

Researcher

Department of Computer Science
Chair of Computer Science 5 (Pattern Recognition)

Martensstr. 3
91058 Erlangen

Email: florian.goldmann@fau.de
Website: https://lme.tf.fau.de/person/goldmann/

Academic CV

Since 02/2023:
PhD Candidate, Pattern Recognition Lab, FAU Erlangen-Nürnberg, in cooperation with Siemens Healthineers AG, SHS DI CT R&D CTC IA, Forchheim
Research topic: Spectral plaque analysis from Photon Counting CT
10/2019 – 07/2022:
Research Assistant, Fraunhofer Institut für Integrierte Schaltungen IIS
12/2018 – 08/2019:
Research Assistant, Johns Hopkins University (Baltimore, MD, USA)
08/2016 – 10/2018:
Research Assistant, Fraunhofer Institut für Integrierte Schaltungen IIS
08/2016 – 10/2018:
Student at FAU Erlangen-Nürnberg, M. Sc. Medical Engineering
08/2008 – 03/2015:
B. Sc. & M. Sc. Chemistry

Projects

No projects found.

Publications

2023

Book Contributions

Goldmann F., Keinert J., Fink L., Gul MSK., Jaschke T., Ziegler M., Baetz M., Fößel S., Prappacher N.:
Light field processing for media applications
In: Immersive Video Technologies, Elsevier, 2023, p. 227--264
BibTeX: Download

2021

Journal Articles

Dreizin D., Goldmann F., LeBedis C., Boscak A., Dattwyler M., Bodanapally U., Li G., Anderson S., Maier A., Unberath M.:
An Automated Deep Learning Method for Tile AO/OTA Pelvic Fracture Severity Grading from Trauma whole-Body CT
In: Journal of Digital Imaging (2021)
ISSN: 0897-1889
DOI: 10.1007/s10278-020-00399-x
BibTeX: Download

2019

Journal Articles

Unberath M., Zaech JN., Gao C., Bier B., Goldmann F., Lee SC., Fotouhi J., Taylor R., Armand M., Navab N.:
Enabling machine learning in X-ray-based procedures via realistic simulation of image formation
In: International Journal of Computer Assisted Radiology and Surgery 14 (2019), p. 1517-1528
ISSN: 1861-6410
DOI: 10.1007/s11548-019-02011-2
BibTeX: Download
Bier B., Goldmann F., Zaech JN., Fotouhi J., Hegeman R., Grupp R., Armand M., Osgood G., Navab N., Maier A., Unberath M.:
Learning to detect anatomical landmarks of the pelvis in X-rays from arbitrary views
In: International Journal of Computer Assisted Radiology and Surgery (2019)
ISSN: 1861-6410
DOI: 10.1007/s11548-019-01975-5
BibTeX: Download

Student theses

Type	Title	Status
BA thesis	CT Material Decomposition with Deep Learning	running