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.

  • Oct. 2021 – now
    Ph.D., Friedrich-Alexander-Universität Erlangen-Nürnberg,
    Department for Artificial Intelligence in Biomedical Engineering,
    Chair for Health Data Science
    • Research Stay at King’s College London (Oct. 2024 – Apr. 2025), DAAD Research Stipend
  • 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

  • Schröder, C., Stegmeier, M., Müller, J. P., Day, T., Cuomo, M., Dewald, O., … & Dittrich, S. (2025). Automatic Segmentation of Lymphatic Perfusion Pattern in Fontan Patients. The Thoracic and Cardiovascular Surgeon, 73(S 02), DGPK-KV14.
  • Müller, J. P., & Kainz, B. (2024, October). Resource-efficient medical image analysis with self-adapting forward-forward networks. In International Workshop on Machine Learning in Medical Imaging (pp. 180-190). Cham: Springer Nature Switzerland.
  • Baugh, M., Reynaud, H., Marimont, S. N., Cechnicka, S., Müller, J. P., Tarroni, G., & Kainz, B. (2024, October). Image-Conditioned Diffusion Models for Medical Anomaly Detection. In International Workshop on Uncertainty for Safe Utilization of Machine Learning in Medical Imaging (pp. 117-127). Cham: Springer Nature Switzerland.
  • Erick, F. X., Rezaei, M., Müller, J. P., & Kainz, B. (2024, October). Uncertainty-Aware Vision Transformers for Medical Image Analysis. In International Workshop on Uncertainty for Safe Utilization of Machine Learning in Medical Imaging (pp. 171-180). Cham: Springer Nature Switzerland.
  • Dombrowski, M., Reynaud, H., Müller, J.P., Baugh, M. and Kainz, B., 2024, March. Trade-offs in fine-tuned diffusion models between accuracy and interpretability. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 38, No. 19, pp. 21037-21045).
  • Stegmaier, M., Müller, J.P., Schröder, C., Day, T., Cuomo, M., Dewald, O., Dittrich, S. and Kainz, B., 2024, February. Automatic Segmentation of Lymphatic Perfusion in Patients with Congenital Single Ventricle Defects. In BVM Workshop (pp. 255-260). Wiesbaden: Springer Fachmedien Wiesbaden.
  • 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. and Kainz, B., 2023, October. Many tasks make light work: Learning to localise medical anomalies from multiple synthetic tasks. In International Conference on Medical Image Computing and Computer-Assisted Intervention (pp. 162-172). Cham: Springer Nature Switzerland.
  • 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. In: Sudre, C.H., Baumgartner, C.F., Dalca, A., Mehta, R., Qin, C., Wells, W.M. (eds) Uncertainty for Safe Utilization of Machine Learning in Medical Imaging. UNSURE 2023. Lecture Notes in Computer Science, vol 14291. Springer, Cham. https://doi.org/10.1007/978-3-031-44336-7_18
  • 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

  • Annotation and Curation of a Novel Fontan MRI Dataset with ensuing Segmentation of Lymphatic Perfusion Patterns using nnU-Net (2023)
  • Mind the Gap: Distance Aware Attention Transformers for more robust self-supervised learning (2023)
  • Detecting the Unexpected: Deep Self-Supervised Anomaly Detection and Localization for AI-Driven Quality Control in Industrial Computer Vision (2023)
  • Real-Time Camera-Based Out-of-Distribution Data Detection in Autonomous Driving (2023)
  • Reduction of Blurring Effect in X-ray projections due to System motion (using Deep Learning Methods) (2023)
  • Breast Cancer Detection in Core-needle Biopsies with Neural Networks (2023)
  • Learnable Slice-to-Volume Reconstruction for Motion Compensation in Fetal Magnetic Resonance Imaging (2022)