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Dr.-Ing. Marc Aubreville

Marc Aubreville

  • Job title: Researcher
  • Organization: Department of Computer Science
  • Working group: Chair of Computer Science 5 (Pattern Recognition)
  • Phone number: +49 9131 85 27891
  • Fax number: +49 9131 85 27270
  • Email: marc.aubreville@fau.de
  • Website:
  • Address:
    Martensstr. 3
    91058 Erlangen
    Room 09.157

 

Academic career:

  • Since 05/2020:
    Post-doc at the pattern recognition lab.
  • 01/2016 – 05/2020:
    Researcher and PhD student at the pattern recognition lab. Main fields of interest include image processing and recognition of brightfield microscopy and confocal laser endomicroscopy images.
  • 10/2003 – 11/2009:
    Student (Electrical Engineering and Information Technology) at Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany.
    Focus topics: Biomedical engineering and signal processing.

Professional career:

  • Since 04/2017:
    System architect, Sivantos GmbH, Erlangen, Germany
    Interface design, technical coordination of projects, hearing aid system architecture development, requirements engineering
  • 02/2010 – 03/2017:
    R&D engineer in signal processing, Sivantos GmbH, Erlangen, Germany.
    Development of hearing instrument signal processing algorithms, from prototyping (MATLAB/Simulink) to product implementation (fixpoint C++), firmware/hardware co-design, coordination of international research projects

2019

  • Deep Learning based Noise Reduction for Hearing Aids

    (Third Party Funds Single)

    Term: February 1, 2019 - January 31, 2022
    Funding source: Industrie
     

    Reduction of unwanted environmental noises is an important feature of today’s hearing aids, which is why noise reduction is nowadays included in almost every commercially available device. The majority of these algorithms, however, is restricted to the reduction of stationary noises. Due to the large number of different background noises in daily situations, it is hard to heuristically cover the complete solution space of noise reduction schemes. Deep learning-based algorithms pose a possible solution to this dilemma, however, they sometimes lack robustness and applicability in the strict context of hearing aids.
    In this project we investigate several deep learning.based methods for noise reduction under the constraints of modern hearing aids. This involves a low latency processing as well as the employing a hearing instrument-grade filter bank. Another important aim is the robustness of the developed methods. Therefore, the methods will be applied to real-world noise signals recorded with hearing instruments.

2017

  • Digital Pathology - New Approaches to the Automated Image Analysis of Histologic Slides

    (Own Funds)

    Term: since January 16, 2017

    The pathologist is still the gold standard in the diagnosis of diseases in tissue slides. Due to its human nature, the pathologist is on one side able to flexibly adapt to the high morphological and technical variability of histologic slides but of limited objectivity due to cognitive and visual traps.

    In diverse project we are applying and validating currently available tools and solutions in digital pathology but are also developing new solution in automated image analysis to complement and improve the pathologist especially in areas of quantitative image analysis.

2014

  • Automatic classification and image analysis of confocal laser endomicroscopy images

    (Own Funds)

    Term: since October 1, 2014

    The goal of this project is to detect cancerous tissue in confocal lasermicroendoscopy (CLE) images of the oral cavity and the vocal cord. The current treatment of these diseases is a histological analysis of specimen and a surgical resection, which has a rather high long-term survival rate, or radiation therapy with a lower survival rate. An early detection of cancerous tissue could lead to a lowered complication rate for further treatment, as well as a better overall prognosis for patients. Further, an in-vivo diagnosis during operation could narrow down the area for the necessary surgical excision, which is especially beneficial for cancer of the vocal cords.

    For this reason, we are applying methods of pattern recognition to facilitate and support diagnosis. We were able to show that these can be applied with high accuracies on CLE images.

2020

Journal Articles

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Thesis

2019

Journal Articles

Book Contributions

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2018

Authored Books

Book Contributions

Conference Contributions

2017

Journal Articles

Conference Contributions