6 citations · 6 across the 6 of their papers we have counts for
8 papers
Prior-Fitted and Frozen Scorers for Multimodal Contrastive Learning
Tillmann Rheude, Roland Eils, Benjamin Wild
Multimodal contrastive learning typically relies on manually specified scoring functions, such as cosine similarity, to quantify the similarity between representations from differe…
Beyond Objective Expressivity: Geometry Preservation in Multimodal Contrastive Learning
Tillmann Rheude, Roland Eils, Benjamin Wild
Contrastive learning is increasingly moving toward settings with three or more modalities instead of image-text pairs. Yet, extending models from pairwise to higher-order multimoda…
Modeling Local, Global, and Cross-Modal Context in Multimodal 3D MRI
Minh Duc Do, Tillmann Rheude, Noel Kronenberg +2
Brain MRI poses a fundamental challenge for machine learning: models must learn from high-dimensional 3D data spanning multiple co-registered modalities, despite the limited sample…
Hidden in the Multiplicative Interaction: Uncovering Fragility in Multimodal Contrastive Learning
Tillmann Rheude, Stefan Hegselmann, Roland Eils +1
Contrastive learning has become a standard approach for unsupervised learning from paired data, as demonstrated by CLIP for image-text matching. However, many domains involve more…
Fusion or Confusion? Multimodal Complexity Is Not All You Need
Tillmann Rheude, Roland Eils, Benjamin Wild
Multimodal learning has become a prominent research area, with the potential of substantial performance gains by combining information across modalities. At the same time, model de…
Cohort-Based Active Modality Acquisition
Tillmann Rheude, Roland Eils, Benjamin Wild
Real-world multimodal machine learning often faces missing, costly-to-acquire modalities, raising the problem of which samples to prioritize for additional acquisition under a budg…
Large Language Models are Powerful Electronic Health Record Encoders
Stefan Hegselmann, Georg von Arnim, Tillmann Rheude +5
Electronic Health Records (EHRs) offer considerable potential for clinical prediction, but their complexity and heterogeneity challenge traditional machine learning. Domain-specifi…
Leveraging CAM Algorithms for Explaining Medical Semantic Segmentation
Tillmann Rheude, Andreas Wirtz, Arjan Kuijper +1
Convolutional neural networks (CNNs) achieve prevailing results in segmentation tasks nowadays and represent the state-of-the-art for image-based analysis. However, the understandi…