1 citations · 1 across the 4 of their papers we have counts for
9 papers
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…
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…