Thomas Wolfers

Deep Unsupervised Anomaly Detection in Brain Imaging: Large-Scale Benchmarking and Bias Analysis

Deep unsupervised anomaly detection in brain magnetic resonance imaging offers a promising route to identify pathological deviations without requiring lesion-specific annotations. Yet, fragmented...

Hierarchical Bayesian Regression for Multi-Site Normative Modeling of Neuroimaging Data

Clinical neuroimaging has recently witnessed explosive growth in data availability which brings studying heterogeneity in clinical cohorts to the spotlight. Normative modeling is an emerging...

Beyond Site Agreement: Re-estimation for Brain Network Generalization

Cross-site out-of-distribution (OOD) generalization in resting-state functional magnetic resonance imaging (rs-fMRI) often relies on learning task-discriminative representations from full-scan...

Learning Cortical Anomaly through Masked Encoding for Unsupervised Heterogeneity Mapping

To which reference class do you belong? Measuring racial fairness of reference classes with normative modeling

Hierarchical Characterization of Brain Dynamics via State Space-Based Vector Quantization

Hierarchical Bayesian Regression for Multi-site Normative Modeling of Neuroimaging Data

MedSAMix: A Training-Free Model Merging Approach for Medical Image Segmentation

Learning beyond Site Bias for OOD Generalization in Brain Networks

Graph-based learning from functional magnetic resonance imaging (fMRI) has shown strong potential for brain network analysis. However, existing methods often degrade under cross-site...

From Raw Corpora to Domain Benchmarks: Automated Evaluation of LLM Domain Expertise

Accurate domain-specific benchmarking of LLMs is essential, specifically in domains with direct implications for humans, such as law, healthcare, and education. However, existing benchmarks are...

MedSAMix: A Training-Free Model Merging Approach for Medical Image Segmentation

Universal medical image segmentation models have emerged as a promising paradigm due to their strong generalizability across diverse tasks, showing great potential for a wide range of clinical...

Hierarchical Characterization of Brain Dynamics via State Space-based Vector Quantization

Understanding brain dynamics through functional Magnetic Resonance Imaging (fMRI) remains a fundamental challenge in neuroscience, particularly in capturing how the brain transitions between...

Self-Supervised Masked Mesh Learning for Unsupervised Anomaly Detection on 3D Cortical Surfaces

Unsupervised anomaly detection in brain imaging is challenging. In this paper, we propose self-supervised masked mesh learning for unsupervised anomaly detection on 3D cortical surfaces. Our...

To which reference class do you belong? Measuring racial fairness of reference classes with normative modeling

Reference classes in healthcare establish healthy norms, such as pediatric growth charts of height and weight, and are used to chart deviations from these norms which represent potential clinical...

Learning Cortical Anomaly through Masked Encoding for Unsupervised Heterogeneity Mapping

The detection of heterogeneous mental disorders based on brain readouts remains challenging due to the complexity of symptoms and the absence of reliable biomarkers. This paper introduces CAM...

Unsupervised Anomaly Detection using Aggregated Normative Diffusion

Early detection of anomalies in medical images such as brain MRI is highly relevant for diagnosis and treatment of many conditions. Supervised machine learning methods are limited to a small...