2 citations · 4 across the 4 of their papers we have counts for
5 papers · 1 filter
JASPR: Joint Spatial Representation learning of histology and spatial genomics for improved virtual genomic screening and clinical prognostication
Marija Pizurica, Eric Zimmermann, Neil Tenenholtz +5
Recent studies have shown that spatial properties of tumors are critical for understanding disease biology and predicting patient outcomes. These spatial properties are increasingl…
Mixed Magnification Aggregation for Generalizable Region-Level Representations in Computational Pathology
Eric Zimmermann, Julian Viret, Michal Zelechowski +7
In recent years, a standard computational pathology workflow has emerged where whole slide images are cropped into tiles, these tiles are processed using a foundation model, and ta…
Virchow2: Scaling Self-Supervised Mixed Magnification Models in Pathology
Eric Zimmermann, Eugene Vorontsov, Julian Viret +11
Foundation models are rapidly being developed for computational pathology applications. However, it remains an open question which factors are most important for downstream perform…
Adapting Self-Supervised Learning for Computational Pathology
Eric Zimmermann, Neil Tenenholtz, James Hall +8
Self-supervised learning (SSL) has emerged as a key technique for training networks that can generalize well to diverse tasks without task-specific supervision. This property makes…
Improving mitosis detection on histopathology images using large vision-language models
Ruiwen Ding, James Hall, Neil Tenenholtz +1
In certain types of cancerous tissue, mitotic count has been shown to be associated with tumor proliferation, poor prognosis, and therapeutic resistance. Due to the high inter-rate…