Johanna Müller

Johanna Müller, M. Sc.

PhD Student & Director of studies @ IDEA Lab

Department Artificial Intelligence in Biomedical Engineering (AIBE)
W3-Professur für Image Data Exploration and Analysis

Werner-von-Siemens Str. 61
91052 Erlangen

Office hours

Please arrange a meeting via e-mail.

Curriculum Vitae (academic)

  • Oct. 2021 – now
    Ph.D., Friedrich-Alexander-Universität Erlangen-Nürnberg,
    Department for Artificial Intelligence in Biomedical Engineering,
    Chair for Health Data Science
  • Oct. 2018 – Jun. 2021
    M.Sc. Simulation Sciences, Rheinisch-Westfälische Technische Hochschule Aachen,
    Faculty of Mechanical Engineering
  • Oct. 2014 – Sep. 2018
    B.Sc. Biosystems Engineering, Otto-von-Guericke University Magdeburg,
    Faculty of Process- and Systems Engineering
  • Sep. 2013 – Sep. 2014
    Voluntary Research Year, Hannover Medical School and Leibniz University Hannover,
    Institute for Multiphase Processes and Centre for Biomedical Engineering


  • Shkëmbi, G., Müller, J. P., Li, Z., Breininger, K., Schüffler, P., & Kainz, B. (2023, October). Whole Slide Multiple Instance Learning for Predicting Axillary Lymph Node Metastasis. In MICCAI Workshop on Data Engineering in Medical Imaging (pp. 11-20). Cham: Springer Nature Switzerland.
  • Baugh, M., Tan, J., Müller, J. P., Dombrowski, M., Batten, J., & Kainz, B. (2023). Many tasks make light work: Learning to localise medical anomalies from multiple synthetic tasks. arXiv preprint arXiv:2307.00899.
  • Baugh, M., Batten, J., Müller, J. P., & Kainz, B. (2023). Zero-Shot Anomaly Detection with Pre-trained Segmentation Models. arXiv preprint arXiv:2306.09269.
  • Jehn, C., Müller, J. P., & Kainz, B. (2023, June). Learnable Slice-to-volume Reconstruction for Motion Compensation in Fetal Magnetic Resonance Imaging. In BVM Workshop (pp. 25-31). Wiesbaden: Springer Fachmedien Wiesbaden.
  • Dombrowski, M., Reynaud, H., Müller, J. P., Baugh, M., & Kainz, B. (2023). Pay Attention: Accuracy Versus Interpretability Trade-off in Fine-tuned Diffusion Models. arXiv preprint arXiv:2303.17908.
  • Müller, J. P., Baugh, M., Tan, J., Dombrowski, M., & Kainz, B. (2023). Confidence-Aware and Self-Supervised Image Anomaly Localisation. arXiv preprint arXiv:2303.13227.
  • Lebbos, C., Barcroft, J., Tan, J., Müller, J. P., Baugh, M., Vlontzos, A., … & Kainz, B. (2022). Adnexal Mass Segmentation with Ultrasound Data Synthesis. In International Workshop on Advances in Simplifying Medical Ultrasound (pp. 106-116). Springer, Cham
  • Baugh, M., Tan, J., Vlontzos, A., Müller, J. P., & Kainz, B. (2022). nnOOD: A Framework for Benchmarking Self-supervised Anomaly Localisation Methods. In International Workshop on Uncertainty for Safe Utilization of Machine Learning in Medical Imaging (pp. 103-112). Springer, Cham.


  • Seminar Advanced Machine Learning for Anomaly Detection
  • Seminar Seminar Humans in the Loop: The Design of Interactive AI Systems
  • Exercise Medical Engineering II
  • Exercise Algorithms, programming, and data representation