6 citations · 10 across the 4 of their papers we have counts for
4 papers
CoMeT: A foundation model for medical image analysis through federated, multidimensional context integration
J. Raphael Schäfer, Kai Geissler, Till Nicke +27
Medical foundation models improve generalization when training AI models with limited labeled data, but remain confined to a single specialty, such as pathology or radiology, and t…
Whole Slide Concepts: A Supervised Foundation Model For Pathological Images
Till Nicke, Daniela Schacherer, Jan Raphael Schäfer +6
Foundation models (FMs) are transforming computational pathology by offering new ways to analyze histopathology images. However, FMs typically require weeks of training on large da…
Tissue Concepts: supervised foundation models in computational pathology
Till Nicke, Jan Raphael Schaefer, Henning Hoefener +4
Due to the increasing workload of pathologists, the need for automation to support diagnostic tasks and quantitative biomarker evaluation is becoming more and more apparent. Founda…
Overcoming Data Scarcity in Biomedical Imaging with a Foundational Multi-Task Model
Raphael Schäfer, Till Nicke, Henning Höfener +6
Foundational models, pretrained on a large scale, have demonstrated substantial success across non-medical domains. However, training these models typically requires large, compreh…