Fabian Wagner

Fabian Wagner, M. Sc.

Researcher

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

Room: Room 09.155
Martensstr. 3
91058 Erlangen

You are working with noisy image data? Try out our open-source, fully Trainable Bilateral Filter Layer (PyTorch) to get rid of noise. It only adds FOUR trainable parameters to your network and you can integrate it in your model the same way as any convolutional layer.

Academic CV

  • Since 10/2020:
    Ph.D. Student at Pattern Recognition Lab, FAU Erlangen-Nürnberg
  • 01/2019 – 01/2020:
    Master’s thesis at Northwestern University, Illinois and University of Wisconsin-Madison
  • 10/2014 – 09/2020:
    B. Sc. and M. Sc. Physics, FAU Erlangen-Nürnberg

Projects

Robust and interpretable low-dose CT denoising in context of the 4D+nanoSCOPE:

  • Advancing osteoporosis medicine by observing bone microstructure and remodelling using a four-dimensional nanoscope

    (Third Party Funds Single)

    Term: April 1, 2019 - March 31, 2025
    Funding source: European Research Council (ERC)
    URL: https://cordis.europa.eu/project/id/810316
    Abstract

    Due to Europe's ageing society, there has been a dramatic increase in the occurrence of osteoporosis (OP) and related diseases. Sufferers have an impaired quality of life, and there is a considerable cost to society associated with the consequent loss of productivity and injuries. The current understanding of this disease needs to be revolutionized, but study has been hampered by a lack of means to properly characterize bone structure, remodeling dynamics and vascular activity. This project, 4D nanoSCOPE, will develop tools and techniques to permit time-resolved imaging and characterization of bone in three spatial dimensions (both in vitro and in vivo), thereby permitting monitoring of bone remodeling and revolutionizing the understanding of bone morphology and its function.

    To advance the field, in vivo high-resolution studies of living bone are essential, but existing techniques are not capable of this. By combining state-of-the art image processing software with innovative 'precision learning' software methods to compensate for artefacts (due e.g. to the subject breathing or twitching), and innovative X-ray microscope hardware which together will greatly speed up image acquisition (aim is a factor of 100), the project will enable in vivo X-ray microscopy studies of small animals (mice) for the first time. The time series of three-dimensional X-ray images will be complemented by correlative microscopy and spectroscopy techniques (with new software) to thoroughly characterize (serial) bone sections ex vivo.

    The resulting three-dimensional datasets combining structure, chemical composition, transport velocities and local strength will be used by the PIs and international collaborators to study the dynamics of bone microstructure. This will be the first time that this has been possible in living creatures, enabling an assessment of the effects on bone of age, hormones, inflammation and treatment.

Publications

2023

Journal Articles

Conference Contributions

2022

Journal Articles

Conference Contributions

2021

Journal Articles

Conference Contributions

2020

Journal Articles

Conference Contributions

Lectures

  • Projekt Flat-Panel CT Reconstruction (ProjFCR): Do 10:00 – 12:00, 2 SWS (5 ECTS)

Student theses

Type Title Status
MA thesis Diffusion-based Super Resolution for X-ray Microscopy finished
MA thesis Classification of Detector Artifacts in Angiographic Imaging using Neural Networks finished
Project Super-short Scans in Bone XRM Acquisitions finished
Project Sparse-angle CT Super Resolution using Known Operators running
Project Deep Learning-based XRM Projection Super Resolution finished
Project Unsupervised Super Resolution in X-ray Microscopy Using a Cycle-Consistent Generative Model finished
MA thesis Learning-based reduction of non-significant changes in subtraction volumes finished
MA thesis Micro CT Denoising Using Low Parameter Models finished