Research Group Carsten Marr
Carsten Marr
Prof. Dr.
Core PI
Carsten Marr
is Professor of Artificial Intelligence in Cell Therapy and Hematology at LMU Munich and Director of the Institute of AI for Health at Helmholtz Munich.
His lab focuses on improving the diagnosis, treatment, and understanding of severe blood disorders. The team develops machine learning algorithms to classify individual cells and patients and uses single-cell data to identify potential drug targets. Combining AI with mechanistic models of haematopoiesis – the production of blood cells – is a key focus of their research.
Team members @MCML
PostDocs
Amirhossein Kardoost
Dr.
→ Group Carsten Marr
Artificial Intelligence in Cell Therapy and Hematology
Akhila Naz Kuppassery Abdulnazar
Dr.
→ Group Carsten Marr
Artificial Intelligence in Cell Therapy and Hematology
Ario Sadafi
→ Group Carsten Marr
Artificial Intelligence in Cell Therapy and Hematology
→ Co-Group Nassir Navab
PhD Students
Christian Brechenmacher
→ Group Carsten Marr
Artificial Intelligence in Cell Therapy and Hematology
Muhammed Furkan Dasdelen
→ Group Carsten Marr
Artificial Intelligence in Cell Therapy and Hematology
Shweta Mahajan
→ Group Carsten Marr
Artificial Intelligence in Cell Therapy and Hematology
Sumin Seo
→ Group Carsten Marr
Artificial Intelligence in Cell Therapy and Hematology
Benjamin Weinert
→ Group Carsten Marr
Artificial Intelligence in Cell Therapy and Hematology
Recent News @MCML
Publications @MCML
2026
Re-mixing Embeddings for Patient Augmentation in Data Scarce Multiple Instance Learning.
MICCAI 2026 - 29th International Conference on Medical Image Computing and Computer Assisted Intervention. Strasbourg, France, Sep 27-Oct 01, 2026. To be published. Preprint available. arXiv GitHub
Genetically Aligned Patient Representations Improve Hematological Diagnosis.
MICCAI 2026 - 29th International Conference on Medical Image Computing and Computer Assisted Intervention. Strasbourg, France, Sep 27-Oct 01, 2026. To be published. Preprint available. arXiv GitHub
Measuring Prediction Uncertainty in Neural Cellular Automata.
MICCAI 2026 - 29th International Conference on Medical Image Computing and Computer Assisted Intervention. Strasbourg, France, Sep 27-Oct 01, 2026. To be published. Preprint available. arXiv GitHub
Pathryoshka: Compressing Pathology Foundation Models via Multi-Teacher Knowledge Distillation with Nested Embeddings.
ECCV 2026 - 19th European Conference on Computer Vision. Malmö, Sweden, Sep 08-12, 2026. DOI
HASSL: Hierarchy-Aware Self-Supervised Learning Framework for Single Cell Microscopy.
ECCV 2026 - 19th European Conference on Computer Vision. Malmö, Sweden, Sep 08-12, 2026. DOI
Are Object-Centric Representations Better at Compositional Generalization?
ICML 2026 - 43rd International Conference on Machine Learning. Seoul, South Korea, Jul 06-11, 2026. To be published. Preprint available. URL
MOSAIC: Maximizing out-of-distribution sensitivity via aligned image classification.
Computer Vision and Image Understanding 271.104885. Jul. 2026. DOI
Illusion of competence: vision–language models provide confident but inaccurate explanations in cytological diagnostics.
Scientific Reports 16.20526. Jul. 2026. DOI GitHub
A Multicenter Benchmark of Multiple Instance Learning Models for Lymphoma Subtyping from HE-stained Whole Slide Images.
Workshop @CVPR 2026 - Workshop at the IEEE/CVF Conference on Computer Vision and Pattern Recognition. Denver, CO, USA, Jun 03-07, 2026. To be published. Preprint available. URL GitHub
AI-based hematological malignancy prediction from peripheral blood smears in a large diagnostic laboratory cohort.
Leukemia 40.6. Jun. 2026. DOI
3D Masked Autoencoders are Robust Learners of Volumetric and Multimodal Cellular Representations for Microscopy.
Preprint (Jun. 2026). arXiv URL
EchoVisuALL: From Echocardiography to Gene Discovery.
Preprint (Feb. 2026). DOI
2025
A large expert-annotated single-cell peripheral blood dataset for hematological disease diagnostics.
Scientific Data 12.1773. Nov. 2025. DOI
CytoSAE: Interpretable Cell Embeddings for Hematology.
MICCAI 2025 - 28th International Conference on Medical Image Computing and Computer Assisted Intervention. Daejeon, Republic of Korea, Sep 23-27, 2025. DOI GitHub
Attention Pooling Enhances NCA-based Classification of Microscopy Images.
MLMI @MICCAI 2025 - 16th International Workshop on Machine Learning in Medical Imaging at the 28th International Conference on Medical Image Computing and Computer Assisted Intervention. Daejeon, Republic of Korea, Sep 23-27, 2025. DOI
Topological Inductive Bias fosters Multiple Instance Learning in Data-Scarce Scenarios.
Transactions on Machine Learning Research Vol. 2025. Feb. 2025. URL GitHub
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2025-10-20 - Last modified: 2026-09-25