Soroosh Tayebi Arasteh

• Since 2024: Postdoctoral Researcher at the Pattern Recognition Lab
• 2024: Doctor of Theoretical Medicine (Dr. rer. medic.), RWTH Aachen University, Aachen, Germany
• 2021: M.Sc. Thesis in Medical Image and Data Processing, Harvard Medical School, Boston, MA, USA
• 2021: M.Sc. in Communications and Multimedia Engineering, FAU Erlangen-Nürnberg, Erlangen, Germany
• 2017: B.Sc. in Electrical Engineering, Bu-Ali Sina University, Hamedan, Iran

Please visit my website for an up-to-date list of journals.

Selected Journal Publications

• 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)
• S. Tayebi Arasteh, R. Siepmann, M. Huppertz, M. Lotfinia, B. Puladi, C. Kuhl, D. Truhn, S. Nebelung. “The Treasure Trove Hidden in Plain Sight: The Utility of GPT-4 in Chest Radiograph Evaluation.” Radiology (2024)
• S. Tayebi Arasteh, T. Arias Vergara, P. Perez, T. Weise, K. Packhäuser, M. Schuster, E. Noeth, A. Maier, S.H. Yang. “Addressing challenges in speaker anonymization to maintain utility while ensuring privacy of pathological speech.” Communications Medicine (2024)
• 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)
• 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)
• 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)
• 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)
• S. Tayebi Arasteh, P. Isfort, M. Saehn, G. Mueller-Franzes, F. Khader, J.N. Kather, C. Kuhl, S. Nebelung, D. Truhn. “Collaborative training of medical artificial intelligence models with non-uniform labels.” Scientific Reports (2023)
• 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)
• 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)
• 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)
• 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)
• 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)
• 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)

Please visit my website for an up-to-date list of journals.

2024

• S. Tayebi Arasteh, A. Ziller, D. Truhn, G. Kaissis. “Differential Privacy in Large-Scale AI Models: Ensuring Fairness and Diagnostic Accuracy in Medical Imaging.” TPDP 2024 – Theory and Practice of Differential Privacy, Boston, MA, USA, August 2024
• 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

2023

• 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

2022

• 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