13 citations · 25 across the 10 of their papers we have counts for
6 papers · 1 filter
DinoBloom: A Foundation Model for Generalizable Cell Embeddings in Hematology
Valentin Koch, Sophia J. Wagner, Salome Kazeminia +5
In hematology, computational models offer significant potential to improve diagnostic accuracy, streamline workflows, and reduce the tedious work of analyzing single cells in perip…
B-Cos Aligned Transformers Learn Human-Interpretable Features
Manuel Tran, Amal Lahiani, Yashin Dicente Cid +7
Vision Transformers (ViTs) and Swin Transformers (Swin) are currently state-of-the-art in computational pathology. However, domain experts are still reluctant to use these models d…
Low-resource finetuning of foundation models beats state-of-the-art in histopathology
Benedikt Roth, Valentin Koch, Sophia J. Wagner +3
To handle the large scale of whole slide images in computational pathology, most approaches first tessellate the images into smaller patches, extract features from these patches, a…
Fully transformer-based biomarker prediction from colorectal cancer histology: a large-scale multicentric study
Sophia J. Wagner, Daniel Reisenbüchler, Nicholas P. West +28
Background: Deep learning (DL) can extract predictive and prognostic biomarkers from routine pathology slides in colorectal cancer. For example, a DL test for the diagnosis of micr…
Local Attention Graph-based Transformer for Multi-target Genetic Alteration Prediction
Daniel Reisenbüchler, Sophia J. Wagner, Melanie Boxberg +1
Classical multiple instance learning (MIL) methods are often based on the identical and independent distributed assumption between instances, hence neglecting the potentially rich…
S5CL: Unifying Fully-Supervised, Self-Supervised, and Semi-Supervised Learning Through Hierarchical Contrastive Learning
Manuel Tran, Sophia J. Wagner, Melanie Boxberg +1
In computational pathology, we often face a scarcity of annotations and a large amount of unlabeled data. One method for dealing with this is semi-supervised learning which is comm…