Sebastian Gündel
Sebastian Gündel, M. Eng.
Researcher
Martensstraße 3
91058 Erlangen
Department of Computer Science
Chair of Computer Science 5 (Pattern Recognition)
Martensstraße 3
91058 Erlangen
- Phone number: +49 9131 85 25246
- Fax number: +49 9131 85-27270
- Email: sebastian.guendel@fau.de
- Website: https://lme.tf.fau.de/person/guendel/
- Since 04/2017:
Ph.D. Student at Pattern Recognition Lab, Friedrich-Alexander-University Erlangen-Nuremberg - 2014 – 2016:
Master of Engineering in Electronic and Mechatronic Systems at Technische Hochschule Nuremberg - 2010 – 2014:
Master of Engineering in Electrical Engineering and Information Technology at Technische Hochschule Nuremberg
No projects found.
2022
Journal Articles
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Deep learning-based patient re-identification is able to exploit the biometric nature of medical chest X-ray data
In: Scientific Reports 12 (2022), Article No.: 14851
ISSN: 2045-2322
DOI: 10.1038/s41598-022-19045-3
BibTeX: Download
2021
Journal Articles
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Robust Classification from Noisy Labels: Integrating Additional Knowledge for Chest Radiography Abnormality Assessment
In: Medical Image Analysis (2021)
ISSN: 1361-8415
DOI: 10.1016/j.media.2021.102087
BibTeX: Download
2020
Conference Contributions
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Epoch-wise label attacks for robustness against label noise
In: Thomas Tolxdorff, Thomas M. Deserno, Heinz Handels, Andreas Maier, Klaus H. Maier-Hein, Christoph Palm (ed.): Bildverarbeitung für die Medizin 2020 2020
DOI: 10.1007/978-3-658-29267-6_64
BibTeX: Download - , , , , :
Extracting and Leveraging Nodule Features with Lung Inpainting for Local Feature Augmentation
11th International Workshop, MLMI 2020, Held in Conjunction with MICCAI 2019
In: Liu M., Yan P., Lian C., Cao X. (ed.): Machine Learning in Medical Imaging 2020
DOI: 10.1007/978-3-030-59861-7_51
BibTeX: Download
2019
Conference Contributions
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Learning to recognize abnormalities in chest X-rays with location-aware dense networks
23rd Iberoamerican Congress on Pattern Recognition, CIARP 2018 (Madrid, November 19, 2018 - November 22, 2018)
In: Ruben Vera-Rodriguez, Julian Fierrez, Aythami Morales (ed.): Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 2019
DOI: 10.1007/978-3-030-13469-3_88
BibTeX: Download - , , , , , , , , :
Quantifying and Leveraging Classification Uncertainty for Chest Radiograph Assessment
MICCAI: International Conference on Medical Image Computing and Computer-Assisted Intervention (Shenzhen, China)
In: Dinggang Shen,Tianming Liu,Terry M. Peters, Lawrence H. Staib, Caroline Essert, Sean Zhou, Pew-Thian Yap, Ali Khan (ed.): Medical Image Computing and Computer Assisted Intervention – MICCAI 2019 2019
DOI: 10.1007/978-3-030-32226-7_75
BibTeX: Download
Miscellaneous
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Multi-task Learning for Chest X-ray Abnormality Classification on Noisy Labels
(2019)
URL: https://arxiv.org/abs/1905.06362
BibTeX: Download
(online publication)
| Student | Title | Type | Status |
|---|---|---|---|
| Kai Packhäuser | Analysis of Deep Learning Methods for Re-identification on Chest Radiographs | MA thesis | finished |
| Nicolas von Roden | Prostate Lesion Detection using Multi-Parametric Magnetic Resonance Imaging | MA thesis | finished |
| Adarsh Bhandary Panambur | Lung Nodule Classification in CT Images using Deep Learning | MA thesis | finished |
| Felix Meister | Development of a Fast Biomechanical Cardiac Model for the Treatment Planning of Dilated Cardiomyopathy | MA thesis | finished |
| Lisa Kratzke | Automatic Deep Learning Lung Lesion Characterization with Combined Application of State-of-the-Art Transfer Learning and Image Augmentation Techniques | MA thesis | finished |
| Michael Jechow | Detection of Label Noise in Solar Cell Datasets | BA thesis | finished |