57 citations · 133 across the 9 of their papers we have counts for
9 papers
A Multimodal Foundation Model for Longitudinal Patient Representation and Scalable Insight Generation in Oncology
Eugene Vorontsov, Yi Kan Wang, Alican Bozkurt +13
Precision oncology necessitates a longitudinal model of patient state that captures cancer evolution and treatment over time, integrating multimodal observations. We introduce the…
BIBERT-Pipe on Biomedical Nested Named Entity Linking at BioASQ 2025
Chunyu Li, Xindi Zheng, Siqi Liu
Entity linking (EL) for biomedical text is typically benchmarked on English-only corpora with flat mentions, leaving the more realistic scenario of nested and multilingual mentions…
GraphMMP: A Graph Neural Network Model with Mutual Information and Global Fusion for Multimodal Medical Prognosis
Xuhao Shan, Ruiquan Ge, Jikui Liu +7
In the field of multimodal medical data analysis, leveraging diverse types of data and understanding their hidden relationships continues to be a research focus. The main challenge…
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-…
Screen Them All: High-Throughput Pan-Cancer Genetic and Phenotypic Biomarker Screening from H&E Whole Slide Images
Yi Kan Wang, Ludmila Tydlitatova, Jeremy D. Kunz +15
Molecular assays are standard of care for detecting genomic alterations in cancer prognosis and therapy selection but are costly, tissue-destructive and time-consuming. Artificial…
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…
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…