6 papers
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…
Virchow: A Million-Slide Digital Pathology Foundation Model
Eugene Vorontsov, Alican Bozkurt, Adam Casson +28
The use of artificial intelligence to enable precision medicine and decision support systems through the analysis of pathology images has the potential to revolutionize the diagnos…
Multimodal Alignment Improves Generalizability of Genomic Biomarker Prediction in Computational Pathology
Ekaterina Redekop, Eric Zimmermann, Ava P Amini +5
Computational pathology models that use digitized histopathology whole-slide images have the potential to become a cost-effective and scalable alternative to molecular assays for t…
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…
KerJEPA: Kernel Discrepancies for Euclidean Self-Supervised Learning
Eric Zimmermann, Harley Wiltzer, Justin Szeto +2
Recent breakthroughs in self-supervised Joint-Embedding Predictive Architectures (JEPAs) have established that regularizing Euclidean representations toward isotropic Gaussian prio…
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-…