9 citations · 20 across the 3 of their papers we have counts for
3 papers
Transcriptomics-guided Slide Representation Learning in Computational Pathology
Guillaume Jaume, Lukas Oldenburg, Anurag Vaidya +5
Self-supervised learning (SSL) has been successful in building patch embeddings of small histology images (e.g., 224x224 pixels), but scaling these models to learn slide embeddings…
A General-Purpose Self-Supervised Model for Computational Pathology
Richard J. Chen, Tong Ding, Ming Y. Lu +17
Tissue phenotyping is a fundamental computational pathology (CPath) task in learning objective characterizations of histopathologic biomarkers in anatomic pathology. However, whole…
Utilizing Expert Features for Contrastive Learning of Time-Series Representations
Manuel Nonnenmacher, Lukas Oldenburg, Ingo Steinwart +1
We present an approach that incorporates expert knowledge for time-series representation learning. Our method employs expert features to replace the commonly used data transformati…