234 citations · 508 across the 23 of their papers we have counts for
26 papers
Scalable Training of Spatially Grounded 2D Vision-Language Models for Radiology
Yusuf Salcan, Simon Ging, Robin Tibor Schirrmeister +4
We study how to train visually grounded vision-language models (VLMs) for radiology without manual spatial annotations. We introduce RefRad2D, a large-scale bilingual (German/Engli…
Scaling In-Context Segmentation with Hierarchical Supervision
T. Camaret Ndir, Marco Reisert, Robin T. Schirrmeister
In-context learning (ICL) enables medical image segmentation models to adapt to new anatomical structures from limited examples, reducing the clinical annotation burden. However, s…
Dynamic Prompt Generation for Interactive 3D Medical Image Segmentation Training
Tidiane Camaret Ndir, Alexander Pfefferle, Robin Tibor Schirrmeister
Interactive 3D biomedical image segmentation requires efficient models that can iteratively refine predictions based on user prompts. Current foundation models either lack volumetr…
EEG-CLIP : Learning EEG representations from natural language descriptions
Tidiane Camaret Ndir, Robin Tibor Schirrmeister, Tonio Ball
Deep networks for electroencephalogram (EEG) decoding are often only trained to solve one specific task, such as pathology or age decoding. A more general task-agnostic approach is…
Unlocking In-Context Learning for Natural Datasets Beyond Language Modelling
Jelena Bratulić, Sudhanshu Mittal, David T. Hoffmann +5
Large Language Models (LLMs) exhibit In-Context Learning (ICL), which enables the model to perform new tasks conditioning only on the examples provided in the context without updat…
TuneTables: Context Optimization for Scalable Prior-Data Fitted Networks
Benjamin Feuer, Robin Tibor Schirrmeister, Valeriia Cherepanova +5
While tabular classification has traditionally relied on from-scratch training, a recent breakthrough called prior-data fitted networks (PFNs) challenges this approach. Similar to…
Brain Age Revisited: Investigating the State vs. Trait Hypotheses of EEG-derived Brain-Age Dynamics with Deep Learning
Lukas AW Gemein, Robin T Schirrmeister, Joschka Boedecker +1
The brain's biological age has been considered as a promising candidate for a neurologically significant biomarker. However, recent results based on longitudinal magnetic resonance…
Deep Riemannian Networks for End-to-End EEG Decoding
Daniel Wilson, Robin Tibor Schirrmeister, Lukas Alexander Wilhelm Gemein +1
State-of-the-art performance in electroencephalography (EEG) decoding tasks is currently often achieved with either Deep-Learning (DL) or Riemannian-Geometry-based decoders (RBDs).…
On the Importance of Hyperparameters and Data Augmentation for Self-Supervised Learning
Diane Wagner, Fabio Ferreira, Danny Stoll +3
Self-Supervised Learning (SSL) has become a very active area of Deep Learning research where it is heavily used as a pre-training method for classification and other tasks. However…
When less is more: Simplifying inputs aids neural network understanding
Robin Tibor Schirrmeister, Rosanne Liu, Sara Hooker +1
How do neural network image classifiers respond to simpler and simpler inputs? And what do such responses reveal about the learning process? To answer these questions, we need a cl…
Understanding Anomaly Detection with Deep Invertible Networks through Hierarchies of Distributions and Features
Robin Tibor Schirrmeister, Yuxuan Zhou, Tonio Ball +1
Deep generative networks trained via maximum likelihood on a natural image dataset like CIFAR10 often assign high likelihoods to images from datasets with different objects (e.g.,…
Machine-Learning-Based Diagnostics of EEG Pathology
Lukas Alexander Wilhelm Gemein, Robin Tibor Schirrmeister, Patryk Chrabąszcz +5
Machine learning (ML) methods have the potential to automate clinical EEG analysis. They can be categorized into feature-based (with handcrafted features), and end-to-end approache…
Deep Invertible Networks for EEG-based brain-signal decoding
Robin Tibor Schirrmeister, Tonio Ball
In this manuscript, we investigate deep invertible networks for EEG-based brain signal decoding and find them to generate realistic EEG signals as well as classify novel signals ab…
Deep Learning for micro-Electrocorticographic (μECoG) Data
Xi Wang, C. Alexis Gkogkidis, Robin T. Schirrmeister +6
Machine learning can extract information from neural recordings, e.g., surface EEG, ECoG and μECoG, and therefore plays an important role in many research and clinical applications…
A large-scale evaluation framework for EEG deep learning architectures
Felix A. Heilmeyer, Robin T. Schirrmeister, Lukas D. J. Fiederer +3
EEG is the most common signal source for noninvasive BCI applications. For such applications, the EEG signal needs to be decoded and translated into appropriate actions. A recently…
Cross-paradigm pretraining of convolutional networks improves intracranial EEG decoding
Joos Behncke, Robin Tibor Schirrmeister, Martin Völker +5
When it comes to the classification of brain signals in real-life applications, the training and the prediction data are often described by different distributions. Furthermore, di…
The role of robot design in decoding error-related information from EEG signals of a human observer
Joos Behncke, Robin Tibor Schirrmeister, Wolfram Burgard +1
For utilization of robotic assistive devices in everyday life, means for detection and processing of erroneous robot actions are a focal aspect in the development of collaborative…
EEG-GAN: Generative adversarial networks for electroencephalograhic (EEG) brain signals
Kay Gregor Hartmann, Robin Tibor Schirrmeister, Tonio Ball
Generative adversarial networks (GANs) are recently highly successful in generative applications involving images and start being applied to time series data. Here we describe EEG-…
Training Generative Reversible Networks
Robin Tibor Schirrmeister, Patryk Chrabąszcz, Frank Hutter +1
Generative models with an encoding component such as autoencoders currently receive great interest. However, training of autoencoders is typically complicated by the need to train…
Intracranial Error Detection via Deep Learning
Martin Völker, Jiří Hammer, Robin T. Schirrmeister +6
Deep learning techniques have revolutionized the field of machine learning and were recently successfully applied to various classification problems in noninvasive electroencephalo…
Hierarchical internal representation of spectral features in deep convolutional networks trained for EEG decoding
Kay Gregor Hartmann, Robin Tibor Schirrmeister, Tonio Ball
Recently, there is increasing interest and research on the interpretability of machine learning models, for example how they transform and internally represent EEG signals in Brain…
The signature of robot action success in EEG signals of a human observer: Decoding and visualization using deep convolutional neural networks
Joos Behncke, Robin Tibor Schirrmeister, Wolfram Burgard +1
The importance of robotic assistive devices grows in our work and everyday life. Cooperative scenarios involving both robots and humans require safe human-robot interaction. One im…
Deep Transfer Learning for Error Decoding from Non-Invasive EEG
Martin Völker, Robin T. Schirrmeister, Lukas D. J. Fiederer +2
We recorded high-density EEG in a flanker task experiment (31 subjects) and an online BCI control paradigm (4 subjects). On these datasets, we evaluated the use of transfer learnin…
Brain Responses During Robot-Error Observation
Dominik Welke, Joos Behncke, Marina Hader +6
Brain-controlled robots are a promising new type of assistive device for severely impaired persons. Little is however known about how to optimize the interaction of humans and brai…