Sarah Müller

Sarah Müller
Sarah is a Ph.D. student in the Department of Data Science at the Hertie Institute for AI in Brain Health at the University of Tübingen, an IMPRS-IS scholar, and part of the Cluster of Excellence project "Uncovering the inner structure of medical images through generative modeling". In her work, she aims to investigate the image generation process of medical images to model variation among patient populations and disease developments. Her research interests include representation learning, generative models, and disentanglement.
Generative models can help us understand and explore the main factors of variation in medical images.
01
Müller, S., Koch, L. M., Lensch, H. P., & Berens, P.

Disentangling representations of retinal images with generative models

Oct 01, 2025 | Medical Image Analysis, 105
02
Schmidt, G., Heidrich, H., Berens, P., & Müller, S.

Learning Disease State from Noisy Ordinal Disease Progression Labels.

Sep 20, 2025 | MICCAI 2025
03
Gervelmeyer, J., Müller, S., Huang, Z., & Berens, P.

Fundus Image Toolbox: A Python package for fundus image processing

Apr 03, 2025 | Journal of Open Source Software
04
Gervelmeyer, J., Müller, S., Djoumessi, K., Merle, D., Clark, S. J., Koch, L., & Berens, P.

Interpretable-by-design Deep Survival Analysis for Disease Progression Modeling

Oct 06, 2024 | MICCAI 2024
05
Müller, S., Koch, L. M., Lensch, H., & Berens, P.

A Generative Model Reveals the Influence of Patient Attributes on Fundus Images

May 09, 2022 | In Medical Imaging with Deep Learning.
06
Boreiko, V., Ilanchezian, I., Ayhan, M. S., Müller, S., Koch, L. M., Faber, H., Berens, P. & Hein, M.

Visual explanations for the detection of diabetic retinopathy from retinal fundus images

Feb 02, 2022 | International conference on medical image computing and computer-assisted intervention (MICCAI) (pp. 539-549), Cham: Springer Nature Switzerland