Aug 2026
Five Papers Accepted @ MICCAI 2026
Two main conference papers and three workshop papers accepted at UNSURE, CAPI and AgenticMed.
Johanna P. Müller
Dr.-Ing. · IDEA Lab, Department of Artificial Intelligence in Biomedical Engineering
Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany
Incoming Postdoctoral Research Fellow, King's College London — from September 2026
I build self-supervised and supervised AI that finds rare diseases and under-represented anatomies without labelled examples, working closely with clinicians to make it trustworthy enough for the clinic.
01 News
Recent updates
Aug 2026
Dr.-Ing. completed with distinction
Doctoral thesis, Learning Normative Anatomy under Limited Supervision, at Friedrich-Alexander-Universität Erlangen-Nürnberg.
Jul 2026
MICCAI Outstanding Reviewer Award
Recognised for reviewing at MICCAI 2026.
Jun 2026
Accepted @ MICCAI 2026: Fibers of Asymmetric Similarity
A framework for clinical and imaging data, accepted at MICCAI 2026.
Apr 2026
New Dataset @ISBI 2026: SynthUterus ROI (1.0)
A synthetic dataset of the uterus in four anatomical orientations.
Nov 2025
Best Poster Award
at the Bavarian Conference on AI in Medicine.
Oct 2025
Women in MICCAI Award
Best AI Democratization Paper award at MICCAI 2025.
Jul 2025
Four Papers Accepted @ MICCAI 2025
Main conference and Workshops
PAST
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02 Research
Areas of active work
Working without or rare labels
I hold a doctoral degree from the IDEA Lab at Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), earned within the joint Borderless research group between Imperial College London and FAU, where my doctoral work focused on machine learning for quality control and diagnostic support at the front line of care. From September 2026, I join King's College London as a Postdoctoral Research Fellow in...
- Anomaly & out-of-distribution detection
- Uncertainty quantification
- Generative modelling for medical imaging
- Data- and resource-efficient learning
- Fetal & cardiovascular imaging analysis
03 Selected work
36 total
MICCAI ’26
Fibers of Asymmetric Similarity: A Framework for Clinical and Imaging Data
MICCAI · 2026
@inproceedings{muller2026fibers,
title = {Fibers of Asymmetric Similarity: A Framework for
Clinical and Imaging Data},
author = {M{\"u}ller, Johanna P. and Baugh, M. and Wright, R.
and Day, T. and Rezavi, R. and Kainz, B.},
booktitle = {MICCAI},
year = {2026}
}
ASMUS ’25
L-FUSION: Laplacian Fetal Ultrasound Segmentation and Uncertainty Estimation
Simplifying Medical Ultrasound, ASMUS 2025, eds. Ni D, Noble A, Huang R, Xue W, Springer LNCS vol. 16165, Cham
@inproceedings{muller2026lfusion,
title = {L-FUSION: Laplacian Fetal Ultrasound Segmentation and
Uncertainty Estimation},
author = {M{\"u}ller, Johanna P. and Wright, Robert and Day, Thomas G. and Venturini, Lorenzo and Budd, Samuel F. and Reynaud, Hadrien and Hajnal, Joseph V. and Razavi, Reza and Kainz, Bernhard},
editor = {Ni, D. and Noble, A. and Huang, R. and Xue, W.},
booktitle = {Simplifying Medical Ultrasound},
series = {Springer LNCS},
volume = {16165},
address = {Cham},
year = {2026},
note = {ASMUS 2025}
}
CAPI ’25
Diffusing the Blind Spot: Uterine MRI Synthesis with Diffusion Models
MICCAI CAPI Workshop · 2025
@inproceedings{muller2026diffusing,
title = {Diffusing the Blind Spot: Uterine MRI Synthesis with
Diffusion Models},
author = {M{\"u}ller, Johanna P. and Knupfer, A. and Bl{\"o}ss, P.
and Vittur, E. B. and Kainz, B. and Hutter, J.},
editor = {Celebi, M. E. and others},
booktitle = {Skin Image Analysis, and Computer-Aided Pelvic Imaging
for Female Health},
series = {Springer LNCS},
volume = {16149},
address = {Cham},
year = {2025},
note = {CAPI 2025}
}
AAAI ’24
Trade-offs in Fine-Tuned Diffusion Models Between Accuracy and Interpretability
Proceedings of the AAAI Conference on Artificial Intelligence, 38(19):21037–21045 · 2024
@inproceedings{dombrowski2024tradeoffs,
title = {Trade-offs in Fine-Tuned Diffusion Models Between
Accuracy and Interpretability},
author = {Dombrowski, M. and Reynaud, H. and M{\"u}ller,
Johanna P. and Baugh, M. and Kainz, B.},
booktitle = {Proceedings of the AAAI Conference on Artificial
Intelligence},
volume = {38},
number = {19},
pages = {21037--21045},
year = {2024},
month = mar
}
UNSURE ’23
Confidence-Aware and Self-supervised Image Anomaly Localisation
International Workshop on Uncertainty for Safe Utilization of Machine Learning in Medical Imaging, pp. 177–187, Cham: Springer Nature Switzerland · 2023
Introduces confidence-aware self-supervised localisation for detecting rare or unexpected findings in medical images without labelled abnormal examples. Part of the line of work that won the MICCAI Medical Out-of-Distribution (MOOD) Challenge in 2023 and 2024.
@inproceedings{muller2023confidenceaware,
title = {Confidence-Aware and Self-supervised Image Anomaly
Localisation},
author = {M{\"u}ller, Johanna P. and Baugh, M. and Tan, J.
and Dombrowski, M. and Kainz, B.},
booktitle = {International Workshop on Uncertainty for Safe
Utilization of Machine Learning in Medical Imaging},
publisher = {Springer Nature Switzerland},
address = {Cham},
pages = {177--187},
year = {2023},
month = oct
}
MICCAI ’23
Many Tasks Make Light Work: Learning to Localise Medical Anomalies from Multiple Synthetic Tasks
International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 162–172, Cham: Springer Nature Switzerland · 2023
@inproceedings{baugh2023many,
title = {Many Tasks Make Light Work: Learning to Localise
Medical Anomalies from Multiple Synthetic Tasks},
author = {Baugh, M. and Tan, J. and M{\"u}ller, Johanna P.
and Dombrowski, M. and Batten, J. and Kainz, B.},
booktitle = {International Conference on Medical Image Computing
and Computer-Assisted Intervention},
publisher = {Springer Nature Switzerland},
address = {Cham},
pages = {162--172},
year = {2023},
month = oct
}