Soroosh Tayebi Arasteh

Senior Artificial Intelligence Researcher


Lab for Artificial Intelligence in Medicine, RWTH Aachen University
Department of Diagnostic and Interventional Radiology, University Hospital RWTH Aachen
Pauwelsstr. 30, 52074 Aachen, Germany

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!
Description of Image

May 16, 2024: Celebrating 40 journal peer-reviews 🥳 See Web of Science for more!


Description of Image

March 14, 2024: My journal papers 1, 2, 3, 4, and 5 on diagnostic deep learning using chest radiographs are published!



Description of Image

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

Description of Image

Feb 8, 2024: Celebrating 200 citations on Google Scholar🥳

Description of Image

Jan 17, 2024: Editorial comment on my Radiology AI article by RSNA is available online [Click here]

    "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)

Description of Image

Dec 16, 2023: Final version of the open-source article is now published! [Click here]

    "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)

Description of Image

March 21, 2023: Editorial comment on our Radiology article by RSNA is available online [Click here]

    "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!

  • 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

  • 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

  • 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

  • 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

  • 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

  • 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

  • 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

  • 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

  • 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

  • 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

  • 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

  • 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!