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Research Group Martin Menten


Link to website at TUM

Martin Menten

Dr.

JRG Leader AI for Vision

Artificial Intelligence in Healthcare and Medicine

Martin Menten

leads the MCML Junior Research Group ‘AI for Vision’ at TU Munich.

He and his research group specialize in machine learning for medical imaging. Their research focuses on weakly and self-supervised learning to address data scarcity in healthcare and the integration of multimodal clinical data with medical images. In particular, they are interested in the development and application of machine learning and computer vision algorithms in the field of ophthalmology. Funded by the DFG, the group investigates new research directions that complement and extend MCML’s focus while remaining closely connected to the center.

Team members @MCML

PhD Students

Link to website

Lucie Huang

Artificial Intelligence in Healthcare and Medicine

Link to website

Andrea Posada Cardenas

Artificial Intelligence in Healthcare and Medicine

Recent News @MCML

Link to MCML Researchers With Six Papers at WACV 2025

27.02.2025

MCML Researchers With Six Papers at WACV 2025

Link to MCML Researchers With 39 Papers in Highly-Ranked Journals

01.01.2025

MCML Researchers With 39 Papers in Highly-Ranked Journals

Publications @MCML

2025


[7]
A. H. Berger, L. Lux, A. Weers, M. Menten, D. Rückert and J. C. Paetzold.
Pitfalls of topology-aware image segmentation.
IPMI 2025 - Information Processing in Medical Imaging. Kos Island, Greece, May 25-30, 2025. To be published. Preprint available. arXiv
Abstract

Topological correctness, i.e., the preservation of structural integrity and specific characteristics of shape, is a fundamental requirement for medical imaging tasks, such as neuron or vessel segmentation. Despite the recent surge in topology-aware methods addressing this challenge, their real-world applicability is hindered by flawed benchmarking practices. In this paper, we identify critical pitfalls in model evaluation that include inadequate connectivity choices, overlooked topological artifacts in ground truth annotations, and inappropriate use of evaluation metrics. Through detailed empirical analysis, we uncover these issues’ profound impact on the evaluation and ranking of segmentation methods. Drawing from our findings, we propose a set of actionable recommendations to establish fair and robust evaluation standards for topology-aware medical image segmentation methods.

MCML Authors
Link to website

Laurin Lux

Artificial Intelligence in Healthcare and Medicine

Link to Profile Martin Menten

Martin Menten

Dr.

Artificial Intelligence in Healthcare and Medicine

Link to Profile Daniel Rückert

Daniel Rückert

Prof. Dr.

Artificial Intelligence in Healthcare and Medicine


[6]
L. D. Reyes Vargas, M. Menten, J. C. Paetzold, N. Navab and M. F. Azampour.
Skelite: Compact Neural Networks for Efficient Iterative Skeletonization.
IPMI 2025 - Information Processing in Medical Imaging. Kos Island, Greece, May 25-30, 2025. To be published. Preprint available. arXiv
Abstract

Skeletonization extracts thin representations from images that compactly encode their geometry and topology. These representations have become an important topological prior for preserving connectivity in curvilinear structures, aiding medical tasks like vessel segmentation. Existing compatible skeletonization algorithms face significant trade-offs: morphology-based approaches are computationally efficient but prone to frequent breakages, while topology-preserving methods require substantial computational resources.
We propose a novel framework for training iterative skeletonization algorithms with a learnable component. The framework leverages synthetic data, task-specific augmentation, and a model distillation strategy to learn compact neural networks that produce thin, connected skeletons with a fully differentiable iterative algorithm.
Our method demonstrates a 100 times speedup over topology-constrained algorithms while maintaining high accuracy and generalizing effectively to new domains without fine-tuning. Benchmarking and downstream validation in 2D and 3D tasks demonstrate its computational efficiency and real-world applicability.

MCML Authors
Link to Profile Martin Menten

Martin Menten

Dr.

Artificial Intelligence in Healthcare and Medicine

Link to Profile Nassir Navab

Nassir Navab

Prof. Dr.

Computer Aided Medical Procedures & Augmented Reality

Link to website

Mohammad Farid Azampour

Computer Aided Medical Procedures & Augmented Reality


[5]
Ö. Turgut, P. Müller, P. Hager, S. Shit, S. Starck, M. Menten, E. Martens and D. Rückert.
Unlocking the diagnostic potential of electrocardiograms through information transfer from cardiac magnetic resonance imaging.
Medical Image Analysis 101.103451 (Apr. 2025). DOI GitHub
Abstract

Cardiovascular diseases (CVD) can be diagnosed using various diagnostic modalities. The electrocardiogram (ECG) is a cost-effective and widely available diagnostic aid that provides functional information of the heart. However, its ability to classify and spatially localise CVD is limited. In contrast, cardiac magnetic resonance (CMR) imaging provides detailed structural information of the heart and thus enables evidence-based diagnosis of CVD, but long scan times and high costs limit its use in clinical routine. In this work, we present a deep learning strategy for cost-effective and comprehensive cardiac screening solely from ECG. Our approach combines multimodal contrastive learning with masked data modelling to transfer domain-specific information from CMR imaging to ECG representations. In extensive experiments using data from 40,044 UK Biobank subjects, we demonstrate the utility and generalisability of our method for subject-specific risk prediction of CVD and the prediction of cardiac phenotypes using only ECG data. Specifically, our novel multimodal pre-training paradigm improves performance by up to 12.19% for risk prediction and 27.59% for phenotype prediction. In a qualitative analysis, we demonstrate that our learned ECG representations incorporate information from CMR image regions of interest.

MCML Authors
Link to Profile Martin Menten

Martin Menten

Dr.

Artificial Intelligence in Healthcare and Medicine

Link to Profile Daniel Rückert

Daniel Rückert

Prof. Dr.

Artificial Intelligence in Healthcare and Medicine


[4]
M. Hartenberger, H. Ayaz, F. Ozlugedik, C. Caredda, L. Giannoni, F. Langle, L. Lux, J. Weidner, A. Berger, F. Kofler, M. Menten, B. Montcel, I. Tachtsidis, D. Rückert and I. Ezhov.
Redefining spectral unmixing for in-vivo brain tissue analysis from hyperspectral imaging.
Preprint (Mar. 2025). arXiv
Abstract

In this paper, we propose a methodology for extracting molecular tumor biomarkers from hyperspectral imaging (HSI), an emerging technology for intraoperative tissue assessment. To achieve this, we employ spectral unmixing, allowing to decompose the spectral signals recorded by the HSI camera into their constituent molecular components. Traditional unmixing approaches are based on physical models that establish a relationship between tissue molecules and the recorded spectra. However, these methods commonly assume a linear relationship between the spectra and molecular content, which does not capture the whole complexity of light-matter interaction. To address this limitation, we introduce a novel unmixing procedure that allows to take into account non-linear optical effects while preserving the computational benefits of linear spectral unmixing. We validate our methodology on an in-vivo brain tissue HSI dataset and demonstrate that the extracted molecular information leads to superior classification performance.

MCML Authors
Link to website

Laurin Lux

Artificial Intelligence in Healthcare and Medicine

Link to Profile Martin Menten

Martin Menten

Dr.

Artificial Intelligence in Healthcare and Medicine

Link to Profile Daniel Rückert

Daniel Rückert

Prof. Dr.

Artificial Intelligence in Healthcare and Medicine


[3]
D. Mildenberger, P. Hager, D. Rückert and M. Menten.
A Tale of Two Classes: Adapting Supervised Contrastive Learning to Binary Imbalanced Datasets.
Preprint (Mar. 2025). arXiv
Abstract

Supervised contrastive learning (SupCon) has proven to be a powerful alternative to the standard cross-entropy loss for classification of multi-class balanced datasets. However, it struggles to learn well-conditioned representations of datasets with long-tailed class distributions. This problem is potentially exacerbated for binary imbalanced distributions, which are commonly encountered during many real-world problems such as medical diagnosis. In experiments on seven binary datasets of natural and medical images, we show that the performance of SupCon decreases with increasing class imbalance. To substantiate these findings, we introduce two novel metrics that evaluate the quality of the learned representation space. By measuring the class distribution in local neighborhoods, we are able to uncover structural deficiencies of the representation space that classical metrics cannot detect. Informed by these insights, we propose two new supervised contrastive learning strategies tailored to binary imbalanced datasets that improve the structure of the representation space and increase downstream classification accuracy over standard SupCon by up to 35%. We make our code available.

MCML Authors
Link to website

David Mildenberger

Artificial Intelligence in Healthcare and Medicine

Link to Profile Daniel Rückert

Daniel Rückert

Prof. Dr.

Artificial Intelligence in Healthcare and Medicine

Link to Profile Martin Menten

Martin Menten

Dr.

Artificial Intelligence in Healthcare and Medicine


[2]
A. H. Berger, L. Lux, S. Shit, I. Ezhov, G. Kaissis, M. Menten, D. Rückert and J. C. Paetzold.
Cross-domain and Cross-dimension Learning for Image-to-Graph Transformers.
WACV 2025 - IEEE/CVF Winter Conference on Applications of Computer Vision. Tucson, AZ, USA, Feb 28-Mar 04, 2025. To be published. Preprint available. arXiv
Abstract

Direct image-to-graph transformation is a challenging task that involves solving object detection and relationship prediction in a single model. Due to this task’s complexity, large training datasets are rare in many domains, making the training of deep-learning methods challenging. This data sparsity necessitates transfer learning strategies akin to the state-of-the-art in general computer vision. In this work, we introduce a set of methods enabling cross-domain and cross-dimension learning for image-to-graph transformers. We propose (1) a regularized edge sampling loss to effectively learn object relations in multiple domains with different numbers of edges, (2) a domain adaptation framework for image-to-graph transformers aligning image- and graph-level features from different domains, and (3) a projection function that allows using 2D data for training 3D transformers. We demonstrate our method’s utility in cross-domain and cross-dimension experiments, where we utilize labeled data from 2D road networks for simultaneous learning in vastly different target domains. Our method consistently outperforms standard transfer learning and self-supervised pretraining on challenging benchmarks, such as retinal or whole-brain vessel graph extraction.

MCML Authors
Link to website

Laurin Lux

Artificial Intelligence in Healthcare and Medicine

Georgios Kaissis

Georgios Kaissis

Dr.

* Former Member

Link to Profile Martin Menten

Martin Menten

Dr.

Artificial Intelligence in Healthcare and Medicine

Link to Profile Daniel Rückert

Daniel Rückert

Prof. Dr.

Artificial Intelligence in Healthcare and Medicine


2024


[1]
L. Lux, A. H. Berger, M. Romeo-Tricas, M. Menten, D. Rückert and J. C. Paetzold.
Exploring Graphs as Data Representation for Disease Classification in Ophthalmology.
GRAIL @MICCAI 2024 - 6th Workshop on GRaphs in biomedicAl Image anaLysis at the 27th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2024). Marrakesh, Morocco, Oct 06-10, 2024. DOI URL
Abstract

Interpretability, particularly in terms of human understandable concepts, is essential for building trust in machine learning models for disease classification. However, state-of-the-art image classifiers exhibit limited interpretability, posing a significant barrier to their acceptance in clinical practice. To address this, our work introduces two graph representations of the retinal vasculature, aiming to bridge the gap between high-performance classifiers and human-understandable interpretability concepts in ophthalmology. We use these graphs with the aim of training graph neural networks (GNNs) for disease staging. First, we formally and experimentally show that GNNs can learn known clinical biomarkers. In that, we show that GNNs can learn human interpretable concepts. Next, we train GNNs for disease staging and study how different aggregation strategies lead the GNN to learn more and less human interpretable features. Finally, we propose a visualization for integrated gradients on graphs, which allows us to identify if GNN models have learned human-understandable representations of the data.

MCML Authors
Link to website

Laurin Lux

Artificial Intelligence in Healthcare and Medicine

Link to Profile Martin Menten

Martin Menten

Dr.

Artificial Intelligence in Healthcare and Medicine

Link to Profile Daniel Rückert

Daniel Rückert

Prof. Dr.

Artificial Intelligence in Healthcare and Medicine