13 citations · 25 across the 10 of their papers we have counts for
10 papers
A Large-Scale Benchmark of Cross-Modal Learning for Histology and Gene Expression in Spatial Transcriptomics
Rushin H. Gindra, Giovanni Palla, Mathias Nguyen +6
Spatial transcriptomics enables simultaneous measurement of gene expression and tissue morphology, offering unprecedented insights into cellular organization and disease mechanisms…
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…
Approaching Peak Ground Truth
Florian Kofler, Johannes Wahle, Ivan Ezhov +15
Machine learning models are typically evaluated by computing similarity with reference annotations and trained by maximizing similarity with such. Especially in the biomedical doma…
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…
Multi-Modality Microscopy Image Style Transfer for Nuclei Segmentation
Ye Liu, Sophia J. Wagner, Tingying Peng
Annotating microscopy images for nuclei segmentation is laborious and time-consuming. To leverage the few existing annotations, also across multiple modalities, we propose a novel…
Structure-Preserving Multi-Domain Stain Color Augmentation using Style-Transfer with Disentangled Representations
Sophia J. Wagner, Nadieh Khalili, Raghav Sharma +4
In digital pathology, different staining procedures and scanners cause substantial color variations in whole-slide images (WSIs), especially across different laboratories. These co…