14 citations · 33 across the 36 of their papers we have counts for
9 papers · 1 filter
Stochastic Siamese MAE Pretraining for Longitudinal Medical Images
Taha Emre, Arunava Chakravarty, Thomas Pinetz +9
Temporally aware image representations are crucial for capturing disease progression in 3D volumes of longitudinal medical datasets. However, recent state-of-the-art self-supervise…
Synthetic Vasculature and Pathology Enhance Vision-Language Model Reasoning
Chenjun Li, Cheng Wan, Laurin Lux +4
Vision-Language Models (VLMs) offer a promising path toward interpretable medical diagnosis by allowing users to ask about clinical explanations alongside predictions and across di…
Efficient numeracy in language models through single-token number embeddings
Linus Kreitner, Paul Hager, Jonathan Mengedoht +3
To drive progress in science and engineering, large language models (LLMs) must be able to process large amounts of numerical data and solve long calculations efficiently. This is…
On Arbitrary Predictions from Equally Valid Models
Sarah Lockfisch, Kristian Schwethelm, Martin Menten +4
Model multiplicity refers to the existence of multiple machine learning models that describe the data equally well but may produce different predictions on individual samples. In m…
A Tale of Two Classes: Adapting Supervised Contrastive Learning to Binary Imbalanced Datasets
David Mildenberger, Paul Hager, Daniel Rueckert +1
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…
Skelite: Compact Neural Networks for Efficient Iterative Skeletonization
Luis D. Reyes Vargas, Martin J. Menten, Johannes C. Paetzold +2
Skeletonization extracts thin representations from images that compactly encode their geometry and topology. These representations have become an important topological prior for pr…
Fine-tuning Vision Language Models with Graph-based Knowledge for Explainable Medical Image Analysis
Chenjun Li, Laurin Lux, Alexander H. Berger +3
Accurate staging of Diabetic Retinopathy (DR) is essential for guiding timely interventions and preventing vision loss. However, current staging models are hardly interpretable, an…
Redefining spectral unmixing for in-vivo brain tissue analysis from hyperspectral imaging
Martin Hartenberger, Huzeyfe Ayaz, Fatih Ozlugedik +12
In this paper, we propose a methodology for extracting molecular tumor biomarkers from hyperspectral imaging (HSI), an emerging technology for intraoperative tissue assessment. To…
Interpretable Retinal Disease Prediction Using Biology-Informed Heterogeneous Graph Representations
Laurin Lux, Alexander H. Berger, Maria Romeo Tricas +8
Interpretability is crucial for utilizing machine learning models as clinical decision support tools for medical diagnostics. However, most state-of-the-art image classifiers based…