10 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…
Integrating chemical structures as treatments improves representations of microscopy images for morphological profiling
Yemin Yu, Emre Hayir, Neil Tenenholtz +5
Recent advances in self-supervised deep learning have improved our ability to quantify cellular morphological changes in high-throughput microscopy screens, a process known as morp…
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…
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-…
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…