Five papers accepted at ISBI 2026 Main Conference

Julia A. Schnabel, Cosmin I. Bercea, Maxime Di Folco, Lina Felsner, Stefan Fischer, Ha Young Kim, Jun Li, Anna Reithmeir

Jan 13, 2026 1 min read

Covariance Descriptors Meet General Vision Encoders: Riemannian Deep Learning for Medical Image Classification
Josef Mayr, Anna Reithmeir, Maxime Di Folco, Julia Schnabel
(https://arxiv.org/abs/2511.04190)
LocBAM: Advancing 3D Patch-Based Image Segmentation by Integrating Location Context
Donnate Hooft, Stefan Fischer, Cosmin Bercea, Jan Peeken, Julia Schnabel
(https://arxiv.org/abs/2601.14802)
Learning to Reason About Rare Diseases Through Retrieval-Augmented Agents
Ha Young Kim, Jun Li, Ana Beatriz Solana, Carolin Pirkl, Benedikt Wiestler, Julia Schnabel, Cosmin Bercea
(https://arxiv.org/abs/2511.04720)
Measuring and Aligning Abstraction in Vision-Language Models with Medical Taxonomies
Ben Schaper1, Maxime Di Folco, Bernhard Kainz, Julia Schnabel, Cosmin Bercea
(https://arxiv.org/abs/2601.14827)
Uncertainty-guided Generation of Dark-field Radiographs
Lina Felsner, Henriette Bast, Tina Dorosti, Florian Schaff, Franz Pfeiffer, Daniela Pfeiffer, Julia Schnabel
(http://arxiv.org/abs/2601.15859)

Julia A. Schnabel

Professor for Computational Imaging and AI in Medicine, Director of the Institute of Machine Learning in Biomedical Imaging

My research interests include machine/deep learning, nonlinear motion modeling, as well as multimodal and quantitative imaging, for cancer-, cardiac-, neuro- and perinatal imaging.

Cosmin I. Bercea

Research Scientist

I am a postdoctoral researcher specializing in vision and multimodal learning for medical image analysis, with the current focus on developing vision-language models for generative downstream tasks.

My research interest is the study of the cardiac function via machine learning methods, in particular representation learning methods that aim to acquire low dimensional representation of high dimensional data. I have a strong interest in cardiac remodelling (adaptation of the heart to its environment or a disease), notably the study of the deformation and shape aspects.