Please login first
Janos Vörös  - - - 
Top co-authors See all
Erik Reimhult

112 shared publications

Institute for Biologically inspired materials

Ning-Ping Huang

29 shared publications

State Key Laboratory of Bioelectronics, School of Biological Science and Medical EngineeringSoutheast University Nanjing China

Marco Stampanoni

22 shared publications

Paul Scherrer Institute, ETH

Csaba Forro

15 shared publications

Laboratory of Biosensors and Bioelectronics, ETH Zurich, Gloriastrasse 35, Zurich 8092, Switzerland

Laszlo Demko

14 shared publications

Institute for Biomedical Engineering, ETH Zürich, 8092 Zürich, Switzerland

136
Publications
0
Reads
0
Downloads
466
Citations
Publication Record
Distribution of Articles published per year 
(2003 - 2019)
Total number of journals
published in
 
34
 
Publications See all
PREPRINT-CONTENT 0 Reads 0 Citations UDCT: Unsupervised data to content transformation with histogram-matching cycle-consistent generative adversarial networ... Ihle Johannes Stephan, Andreas Michael Reichmuth, Sophie Gir... Published: 28 February 2019
bioRxiv, doi: 10.1101/563734
DOI See at publisher website ABS Show/hide abstract
The segmentation of images is a common task in a broad range of research fields. To tackle increasingly complex images, artificial intelligence (AI) based approaches have emerged to overcome the shortcomings of traditional feature detection methods. Owing to the fact that most AI research is made publicly accessible and programming the required algorithms is now possible in many popular languages, the use of such approaches is becoming widespread. However, these methods often require data labeled by the researcher to provide a training target for the algorithms to converge to the desired result. This labeling is a limiting factor in many cases and can become prohibitively time consuming. Inspired by Cycle-consistent Generative Adversarial Networks' (cycleGAN) ability to perform style transfer, we outline a method whereby a computer generated set of images is used to segment the true images. We benchmark our unsupervised approach against a state-of-the-art supervised cell-counting network on the VGG Cells dataset and show that it is not only competitive but can also precisely locate individual cells. We demonstrate the power of this method by segmenting bright-field images of cell cultures, a live-dead assay of C.Elegans and X-ray-computed tomography of metallic nanowire meshes.
Article 0 Reads 0 Citations A Versatile Protein and Cell Patterning Method Suitable for Long-Term Neural Cultures Serge Weydert, Sophie Girardin, Xinnan Cui, Stefan Zürcher, ... Published: 15 February 2019
Langmuir, doi: 10.1021/acs.langmuir.8b03730
DOI See at publisher website
Article 0 Reads 1 Citation Modular microstructure design to build neuronal networks of defined functional connectivity Csaba Forró, Greta Thompson-Steckel, Sean Weaver, Serge Weyd... Published: 01 December 2018
Biosensors and Bioelectronics, doi: 10.1016/j.bios.2018.08.075
DOI See at publisher website
Article 4 Reads 0 Citations Image reversal reactive immersion lithography improves the detection limit of focal molography Andreas Frutiger, Cla Duri Tschannen, Yves Blickenstorfer, A... Published: 26 November 2018
Optics Letters, doi: 10.1364/ol.43.005801
DOI See at publisher website
Article 0 Reads 0 Citations Simultaneous scanning ion conductance and atomic force microscopy with a nanopore: Effect of the aperture edge on the io... Livie Dorwling-Carter, Morteza Aramesh, Csaba Forró, Raphael... Published: 07 November 2018
Journal of Applied Physics, doi: 10.1063/1.5053879
DOI See at publisher website
Article 0 Reads 1 Citation Predictive Model for the Electrical Transport within Nanowire Networks Csaba Forró, László Demkó, Serge Weydert, Janos Vörös, Klas ... Published: 06 November 2018
ACS Nano, doi: 10.1021/acsnano.8b05406
DOI See at publisher website
Top