Soroosh Tayebi Arasteh

Soroosh Tayebi Arasteh, M.Sc.

Academic CV

  • Since 02/2024:
    Postdoctoral Researcher at Lab for Artificial Intelligence in Medicine, Uniklinik RWTH Aachen, Germany
  • 10/2021 – 2024:
    Ph.D. Candidate (Dr.-Ing.) in Computer Science at Pattern Recognition Lab, FAU Erlangen-Nürnberg, Germany
  • 02/2022 – 02/2024:
    Ph.D. Candidate (Dr. rer. medic.) in AI in Medical Image Processing, RWTH Aachen University, Germany
  • 08/2020 – 04/2021:
    M.Sc. Thesis in Medical Image and Data Processing, Harvard Medical School, USA
  • 10/2017 – 04/2021:
    M.Sc. in Communications and Multimedia Engineering, FAU Erlangen-Nürnberg, Germany
  • 09/2013 – 09/2017:
    B.Sc. in Electrical Engineering, Bu-Ali Sina University, Iran

Publications

Please visit my Google Scholar profile for an up-to-date list of publications.

Selected Journal Publications

2024

  • S. Tayebi Arasteh, T. Han, M. Lotfinia, C. Kuhl, J.N. Kather, D. Truhn, S. Nebelung. “Large language models streamline automated machine learning for clinical studies.” Nature Communications (2024), 15, 1603. Nature Portfolio, 10.1038/s41467-024-45879-8
  • S. Tayebi Arasteh, A. Ziller, C. Kuhl, M. Makowski, S. Nebelung, R. Braren, D. Rueckert, D. Truhn, G. Kaissis. “Preserving fairness and diagnostic accuracy in private large-scale AI models for medical imaging.” Communications Medicine (2024), 4, 46. Nature Portfolio, 10.1038/s43856-024-00462-6
  • S. Tayebi Arasteh, M. Lotfinia, T. Nolte, M.J. Sähn, P. Isfort, C. Kuhl, S. Nebelung, G. Kaissis, D. Truhn. “Securing Collaborative Medical AI by Using Differential Privacy: Domain Transfer for Classification of Chest Radiographs.” Radiology: Artificial Intelligence (2024), 6(1), e230212. RSNA, 10.1148/ryai.230212
  • D. Truhn*, S. Tayebi Arasteh* (shared first author), et al. “Encrypted federated learning for secure decentralized collaboration in cancer image analysis”. Medical Image Analysis (2024), 92, 103059, 10.1016/j.media.2023.103059
  • S. Tayebi Arasteh, L. Misera, J.N. Kather, D. Truhn, S. Nebelung. “Enhancing diagnostic deep learning via self-supervised pretraining on large-scale, unlabeled non-medical images.” European Radiology Experimental (2024), 8, 10. Springer Nature, 10.1186/s41747-023-00411-3
  • G. Müller-Franzes, F. Khader, S. Tayebi Arasteh, L. Huck, M. Bode, T. Han, T. Lemainque, J.N. Kather, S. Nebelung, C. Kuhl, D. Truhn. “Intraindividual Comparison of Different Methods for Automated BPE Assessment at Breast MRI: A Call for Standardization.” Radiology (2024), 312(1), e232304. RSNA, 10.1148/radiol.232304

2023

  • S. Tayebi Arasteh, C. Kuhl, M.J. Saehn, P. Isfort, D. Truhn, S. Nebelung. “Enhancing domain generalization in the AI-based analysis of chest radiographs with federated learning.” Scientific Reports (2023), 13, 22576. Nature Portfolio, 10.1038/s41598-023-49956-8
  • S. Tayebi Arasteh, P. Isfort, M. Saehn, G. Mueller-Franzes, F. Khader, J.K. Kather, C. Kuhl, S. Nebelung, D. Truhn. “Collaborative training of medical artificial intelligence models with non-uniform labels.” Scientific Reports (2023), 13, 6046. Nature Portfolio, 10.1038/s41598-023-33303-y
  • S. Tayebi Arasteh, T. Weise, M. Schuster, E. Nöth, A.K. Maier, S.H. Yang. “The effect of speech pathology on automatic speaker verification: a large-scale study.” Scientific Reports (2023), 13, 20476. Nature Portfolio, 10.1038/s41598-023-47711-7
  • S. Tayebi Arasteh, J. Romanowicz, D.F. Pace, P. Golland, A.J. Powell, A.K. Maier, D. Truhn, T. Brosch, J. Weese, M. Lotfinia, R.J. van der Geest, M.H. Moghari. “Automated segmentation of 3D cine cardiovascular magnetic resonance imaging.” Frontiers in Cardiovascular Medicine (2023), 10,1167500, 10.3389/fcvm.2023.1167500
  • G. Müller-Franzes, L. Huck, S. Tayebi Arasteh, F. Khader, T. Han, V. Schulz, E. Dethlefsen, J.N. Kather, S. Nebelung, T. Nolte, C. Kuhl, D. Truhn. “Using Machine Learning to Reduce the Need for Contrast Agents in Breast MRI through Synthetic Images.” Radiology (2023), 307(3), e222211. RSNA, 10.1148/radiol.222211
  • F. Khader, G. Müller-Franzes, T. Wang, T. Han, S. Tayebi Arasteh, C. Haarburger, J. Stegmaier, K. Bressem, C. Kuhl, S. Nebelung, J.N. Kather, D. Truhn. “Multimodal Deep Learning for Integrating Chest Radiographs and Clinical Parameters: A Case for Transformers.” Radiology (2023), 309(1), e230806. RSNA, 10.1148/radiol.230806
  • G. Müller-Franzes, J.M. Niehues, F. Khader, S. Tayebi Arasteh, C. Haarburger, C. Kuhl, T. Wang, T. Han, T. Nolte, S. Nebelung, J.N. Kather, D. Truhn. “A multimodal comparison of latent denoising diffusion probabilistic models and generative adversarial networks for medical image synthesis.” Scientific Reports (2023), 13, 12098. Nature Portfolio, 10.1038/s41598-023-39278-0
  • F. Khader, G. Müller-Franzes, S. Tayebi Arasteh, T. Han, C. Haarburger, M. Schulze-Hagen, P. Schad, S. Engelhardt, B. Baeßler, S. Foersch, J. Stegmaier, C. Kuhl, S. Nebelung, J.N. Kather, D. Truhn. “Denoising diffusion probabilistic models for 3D medical image generation.” Scientific Reports (2023), 13, 7303. Nature Portfolio, 10.1038/s41598-023-34341-2
  • F. Khader, J.N. Kather, G. Müller-Franzes, T. Wang, T. Han, S. Tayebi Arasteh, K. Hamesch, K. Bressem, C. Haarburger, J. Stegmaier, C. Kuhl, S. Nebelung, D. Truhn. “Medical transformer for multimodal survival prediction in intensive care: Integration of imaging and non-imaging data.” Scientific Reports (2023), 13, 10666. Nature Portfolio, 10.1038/s41598-023-37835-1
  • G. Müller-Franzes, F. Müller-Franzes, L. Huck, V. Raaff, E. Kemmer, F. Khader, S. Tayebi Arasteh, T. Lemainque, J.N. Kather, S. Nebelung, C. Kuhl, D. Truhn. “Fibroglandular tissue segmentation in breast MRI using vision transformers: a multi-institutional evaluation.” Scientific Reports (2023), 13, 14207. Nature Portfolio, 10.1038/s41598-023-41331-x

Journals Peer-Reviewer For

  1. Nature Communications, Nature Portfolio
  2. Medical Image Analysis, Journal of the MICCAI Society
  3. Eurosurveillance, European Centre for Disease Prevention and Control
  4. IEEE Transactions on Medical Imaging, IEEE
  5. npj Precision Oncology, Nature Portfolio
  6. European Radiology Experimental, SpringerOpen | European Society of Radiology
  7. Computerized Medical Imaging and Graphics, Elsevier
  8. PLoS ONE, Public Library of Science
  9. Advances in Continuous and Discrete Models, SpringerOpen
  10. Journal of Medical Internet Research (JMIR), JMIR Publications
  11. JMIR Medical Education, JMIR Publications
  12. JMIR Medical Informatics, JMIR Publications
  13. JMIR Research Protocols, JMIR Publications
  14. Health Informatics Journal, Sage Publications
  15. IET Signal Processing, IET
  16. IET Image Processing, IET
  17. Healthcare Technology Letters, IET
  18. IET Biometrics, IET
  19. Electronics Letters, IET
  20. IET Networks, IET

Talks

  • S. Tayebi Arasteh, C. Kuhl, D. Truhn, S. Nebelung. “The Future is Collaborative: A Systematic Analysis of Federated Learning and Framework Parameters in the AI-Based Interpretation of Chest Radiographs”. 105. Deutscher Röntgenkongress (105th German X-ray Congress), Wiesbaden, Germany, May 2024
  • S. Tayebi Arasteh, C. Kuhl, S. Nebelung, D. Truhn. “Tapping the Pool of Non-Medical Images for Enhanced AI-Based Chest Radiography Analysis.” 105. Deutscher Röntgenkongress, Wiesbaden, Germany, May 2024
  • G. Mueller-Franzes, S. Tayebi Arasteh, F. Khader, S. Nebelung, C. Kuhl, D. Truhn. “Standardizing Qualitative and Quantitative Breast Parenchymal Enhancement Assessment in Breast MRI.” 109th Radiological Society of North America (RSNA) annual meeting, Chicago, IL, USA, 2023
  • S. Tayebi Arasteh, P. Isfort, C. Kuhl, S. Nebelung, D. Truhn. “Automatic Evaluation of Chest Radiographs – The Data Source Matters, But How Much Exactly?” 104. Deutscher Röntgenkongress, Wiesbaden, Germany, 2023
  • S. Tayebi Arasteh, P. Isfort, Marwin Saehn, C. Kuhl, D. Truhn, S. Nebelung. “Training of AI Models Beyond the Local Dataset Using Federated Learning with 695,000 NonIdentically-Labeled Chest Radiographs.” 104. Deutscher Röntgenkongress, Wiesbaden, Germany, May 2023
  • S. Tayebi Arasteh, J.N. Kather, F. Khader, G. Mueller-Franzes, C. Kuhl, P. Isfort, S. Nebelung, P. Bruners, D. Truhn.
    “Secure Federated Learning for Decentralized Collaboration in Development of AI Models.” 108th RSNA annual meeting, Chicago, IL, USA, Nov-Dec 2022

Lectures

No matching records found.