Welcome
I am an AI Researcher and Electrical Engineer, with academic and industrial experience across diverse technology sectors.
Academic CV
- Ongoing: Ph.D. in Artificial Intelligence in Medicine, RWTH Aachen University, Aachen, Germany
- 2021: M.Sc. Thesis in Medical Image and Data Processing, Harvard Medical School, Boston, MA, USA, Grade: 1.0/1.0 (100%)
- 2021: M.Sc. in Electrical Engineering - Signal Processing & Communications, Friedrich-Alexander-Universität Erlangen-Nürnberg, Germany, GPA: 1.5/1.0 (92%, magna cum laude)
- 2017: B.Sc. in Electrical Engineering, Bu-Ali Sina University, Iran, GPA: 17.15/20 (86%)
- 2013: High-School Diploma in Mathematics & Physics, National Organization for Development of Exceptional Talents (Sampad), Hamedan, Iran, GPA: 18.83/20 (94%)
Key Competencies
- Deep Learning
- Medical Image Analysis
- Computer Vision
- Privacy-Preserving AI
- Generative AI
- Natural & Spoken Language Processing
- Research & Development
News
Click here to see the older news!
Feb 21, 2024: My paper on streamlining clinical studies with LLMs is now published by Nature Communications!
By: S. Tayebi Arasteh, et al. in Nature Communications (IF: 16.6)
By: S. Tayebi Arasteh, et al. in Nature Communications (IF: 16.6)
Feb 8, 2024: Celebrating 200 citations on Google Scholar🥳
Feb 1, 2024: I will be presenting the following 2 abstracts on May 10, 2024 in 105. Deutschen Röntgenkongress (105th German X-ray congress) in Wiesbaden, Germany!
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"Tapping the Pool of Non-Medical Images for Enhanced AI-Based Chest Radiography Analysis"
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"The Future is Collaborative: A Systematic Analysis of Federated Learning and Framework Parameters in the AI-Based Interpretation of Chest Radiographs"
Jan 17, 2024: Editorial comment on my Radiology AI article by RSNA is available online [Click here]
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"Securing Collaborative Medical AI by Using Differential Privacy: Domain Transfer for Classification of Chest Radiographs"
By: S. Tayebi Arasteh, et al. in Radiology: Artificial Intelligence (IF: 9.8)
Dec 16, 2023: Final version of the open-source article is now published! [Click here]
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"Encrypted federated learning for secure decentralized collaboration in cancer image analysis"
By: D. Truhn*, S. Tayebi Arasteh*, et al. in Medical Image Analysis (IF: 10.9)
Jul 16, 2023: Our publications 1 and 2 on applying DDPMs on medical images are available in Scientific Reports!
Kudos to my colleagues Firas Khader and Gustav Müller-Franzes!
March 21, 2023: Editorial comment on our Radiology article by RSNA is available online [Click here]
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"Using Machine Learning to Reduce the Need for Contrast Agents in Breast MRI through Synthetic Images"
By: G. Müller-Franzes, L. Huck, S. Tayebi Arasteh, et al. in Radiology (IF: 19.7)
Featured Publications
Click here for the full list of publications!
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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(1), 1603. Springer Nature, DOI: 10.1038/s41467-024-45879-8
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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(1), 46. Springer Nature, DOI: 10.1038/s43856-024-00462-6
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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, DOI: 10.1148/ryai.230212
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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(1), 10. Springer Nature, DOI: 10.1186/s41747-023-00411-3
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D. Truhn*, S. Tayebi Arasteh* et al. "Encrypted federated learning for secure decentralized collaboration in cancer image analysis". Medical image analysis (2024), 92, 103059. DOI: 10.1016/j.media.2023.103059
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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(1), 22576. Nature Portfolio, DOI: 10.1038/s41598-023-49956-8
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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(1), 6046. Nature Portfolio, DOI: 10.1038/s41598-023-33303-y
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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. DOI: 10.3389/fcvm.2023.1167500
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S. Tayebi Arasteh, T. Weise, M. Schuster, E. Noeth, A.K. Maier, S.H. Yang. "The effect of speech pathology on automatic speaker verification: a large-scale study". Scientific Reports (2023), 13(1), 20476. Nature Portfolio, DOI: 10.1038/s41598-023-47711-7
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S. Tayebi Arasteh, C.D. Rios-Urrego, E. Noeth, A.K. Maier, S.H. Yang, J. Rusz, J.R. Orozco-Arroyave. "Federated learning for secure development of AI models for Parkinson's disease detection using speech from different languages". Proceedings of INTERSPEECH 2023, Dublin, Ireland. DOI: 10.21437/Interspeech.2023-2108
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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(1), 7303. Nature Portfolio, DOI: 10.1038/s41598-023-34341-2
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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, DOI: 10.1148/radiol.230806
- 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, DOI: 10.1148/radiol.222211
FEATURED PEER-REVIEWER ROLES
Click here for the full list of peer-reviews!
- Medical Image Analysis, Journal of the MICCAI Society (1 paper; Since 2024) | IF: 10.9
- Eurosurveillance, European Centre for Disease Prevention and Control (1 paper; Since 2024) | IF: 19.0
- IEEE Transactions on Medical Imaging, IEEE (1 paper; Since 2024) | IF: 10.6
- npj Precision Oncology, Nature Portfolio (1 paper; Since 2023) | IF: 7.9
- European Radiology Experimental, SpringerOpen | European Society of Radiology (6 papers; Since 2023) | IF: 3.8
- Computerized Medical Imaging and Graphics, Elsevier (1 paper; Since 2024) | IF: 5.7
- PLoS ONE, Public Library of Science since (1 paper; Since 2024) | IF: 3.7
- Advances in Continuous and Discrete Models, SpringerOpen (3 papers; 2021 - 2022) | IF: 4.1
- Health Informatics Journal, Sage Publications (1 paper; Since 2024) | IF: 3.0