52 citations · 87 across the 7 of their papers we have counts for
4 papers · 1 filter
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…
PRISM: A Multi-Modal Generative Foundation Model for Slide-Level Histopathology
George Shaikovski, Adam Casson, Kristen Severson +19
Foundation models in computational pathology promise to unlock the development of new clinical decision support systems and models for precision medicine. However, there is a misma…
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…
Tag-LLM: Repurposing General-Purpose LLMs for Specialized Domains
Junhong Shen, Neil Tenenholtz, James Brian Hall +2
Large Language Models (LLMs) have demonstrated remarkable proficiency in understanding and generating natural language. However, their capabilities wane in highly specialized domai…