2 citations · 2 across the 1 of their papers we have counts for
3 papers
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…
PRISM2: Unlocking Multi-Modal General Pathology AI with Clinical Dialogue
Eugene Vorontsov, George Shaikovski, Adam Casson +16
Recent rapid progress in the field of computational pathology has been enabled by foundation models. These models are beginning to move beyond encoding image patches towards whole-…
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…