M.Sc.

Karim Armanious

Wiss. Mitarbeiter
Institut für Signalverarbeitung und Systemtheorie

Contact

0049 711 685-67337
0049 711 685-67315

Pfaffenwaldring 47
70569 Stuttgart
Deutschland
Room: 2.251

Office Hours

by arrangement

Subject

Deep Learning / Generative Adversarial Networks (GANs)

Generative Adversarial Networks (GANs) is a new branch of deep learning which was developed recently in 2015. It is being hailed by many field experts as “the next frontier in deep learning” due to its potential in unsupervised learning (learning without labeled data) and its ability to create a common framework for several applications with no hand-crafted loss functions.

In GANs, two networks are pitted against each other and trained jointly. The generator network
acts as a team of counterfeiters trying to generate fake data that resembles the input data without detection. On the other side, the discriminator network is analogous to the police, trying to detect the counterfeit data. Competition drives both networks to improve their methods and learn more about the features of the input data.

GANs have been recently applied successfully in several applications including unsupervised image translation, domain adaptation, image in-painting and semi-supervised classification.

Emphases are:

  • Development and optimization of new GAN algorithms
  • Application of GAN methods for generation, domain-adaptation, and analysis of medical images including MRI, CT, and PET
  • Application of generative frameworks on autonomous driving and acoustic applications

2019

Armanious, K.; Küstner, T.; Reimold, M.; Nikolaou, K.; La, C. F.; Yang, B. & Gatidis, S.
Independent brain 18F-FDG PET attenuation correction using a deep learning approach with Generative Adversarial Networks.
Hellenic journal of nuclear medicine, 2019
 
Armanious, K.; Küstner, T.; Yang, B. & Gatidis, S.
Adversarial inpainting of MR images using deep adversarial networks
Proceedings of the European Society for Magnetic Resonance in Medicine (ESMRMB), 2019
 
Küstner, T.; Mo, K.; Yang, B.; Schick, F.; Gatidis, S. & Armanious, K.
Retrospective deep learning based motion correction from complex-valued imaging data
Proceedings of the European Society for Magnetic Resonance in Medicine (ESMRMB), 2019
Certificate of Merit Award
 
Armanious, K.; Jiang, C.; Abdulatif, S.; Küstner, T.; Gatidis, S. & Yang, B.
Unsupervised Medical Image Translation Using Cycle-MedGAN
Proceedings of the IEEE European Signal Processing Conference EUSIPCO 2019, A Coruna, Spain, September 2019
 
Liebgott, A.; Hinderer, D.; Armanious, A.; Bartler, A.; Nikolaou, K.; Gatidis, S.; and Yang, B.
Prediction of FDG uptake in Lung Tumors from CT Images Using Generative Adversarial Networks
Proceedings of the IEEE European Signal Processing Conference EUSIPCO 2019, A Coruna, Spain, September 2019
 
Armanious, K.; Abdulatif, S.; Aziz, F.; Schneider, U. & Yang, B.
An Adversarial Super-Resolution Remedy for Radar Design Trade-offs
Proceedings of the IEEE European Signal Processing Conference EUSIPCO 2019, A Coruna, Spain, September 2019
 
Fallah, F; Armanious, K; Yang, B.; Bamberg F.
Spatial and Hierarchical Riemannian Dimensionality Reduction and Dictionary Learning for Segmenting Multichannel Images
Proceedings of the IEEE European Signal Processing Conference EUSIPCO 2019, A Coruna, Spain, September 2019
 
Fallah, F; Armanious, K; Yang, B.; Bamberg F.
Volumetric Surface-guided Graph-based Segmentation of Cardiac Adipose Tissues on Fat-Water MR Images
Proceedings of the IEEE European Signal Processing Conference EUSIPCO 2019, A Coruna, Spain, September 2019
 
Armanious, K.; Gatidis, S.; Nikolaou, K.; Yang, B. & Küstner, T.
Retrospective correction of Rigid and Non-Rigid MR motion artifacts using GANs
Proceedings of the IEEE International Symposium on Biomedical Imaging ISBI 2019, Venice, Italy, 2019
 
Hakobyan, G.; Armanious, K.; Yang, B. 
Interference-Aware Cognitive Radar: A Remedy to the Automotive Interference Problem
IEEE Transactions on Aerospace and Electronic Systems, 2019
 
Armanious, K.; Mecky, Y.; Gatidis, Y.; Yang, B. 
Adversarial Inpainting of Medical Image Modalities
Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Brighton, England, 2019
 
Abdulatif, S.; Aziz, F.; Armanious, K.; Kleiner, K.; Yang, B.; Schneider, U. 
Person Identification and Body Mass Index: A Deep Learning-Based Study on Micro-Dopplers
Proceedings of the IEEE Radar Conference (RadarConf), Boston, USA, 2019
 
Küstner, T., Yang, B., Schick, F., Gatidis, S. & Armanious, K.
Retrospective motion correction using deep learning.
Proceedings of the International Society for Magnetic Resonance in Medicine (ISMRM), May, Montreal, Canada, 2019.
Magna Cum Laude Paper Award
 
Armanious, K.; Döbler, M.; Yang, B.; Gatidis, S. 
Generation of globally consistent non-confidential MRI data using deep generative adversarial networks
Proceedings of the International Society for Magnetic Resonance in Medicine (ISMRM), May, Montreal, Canada, 2019.
 
Küstner, T.; Armanious, K.; Yang, J.; Yang, B.; Schick, F. & Gatidis, S.
Retrospective correction of motion-affected MR images using deep learning frameworks
Magnetic Resonance in Medicine, 2019
 
Abdulatif, S.; Armanious, K.; Aziz, F.; Schneider, U. & Yang, B.
Towards Adversarial Denoising of Radar Micro-Doppler Signatures
Proceedings of the IEEE International Radar Conference Radar 2019, Toulon, France, 2019
 
2018
 
Armanious, K.; Küstner, T.; Yang, B.; Gatidis, S. 
Restoration of motion-corrupted MR images using Deep Adversarial Networks
Proceedings of the Annual Meeting RSNA, November 2018, Chicago, USA.
 
Armanious, K.; Fischer, K.; Yang, B.; Gatidis, S. 
Independent PET Attenuation Correction using Conditional Generative Adversarial Networks
Proceedings of the Annual Meeting RSNA, November 2018, Chicago, USA.
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