PRISM: A Multi-Modal Generative Foundation Model for Slide-Level Histopathology

no code implementations • 16 May 2024 • George Shaikovski, Adam Casson, Kristen Severson, Eric Zimmermann, Yi Kan Wang, Jeremy D. Kunz, Juan A. Retamero, Gerard Oakley, David Klimstra, Christopher Kanan, Matthew Hanna, Michal Zelechowski, Julian Viret, Neil Tenenholtz, James Hall, Nicolo Fusi, Razik Yousfi, Peter Hamilton, William A. Moye, Eugene Vorontsov, SiQi Liu, Thomas J. Fuchs

Foundation models in computational pathology promise to unlock the development of new clinical decision support systems and models for precision medicine.

whole slide images

Adapting Self-Supervised Learning for Computational Pathology

no code implementations • 2 May 2024 • Eric Zimmermann, Neil Tenenholtz, James Hall, George Shaikovski, Michal Zelechowski, Adam Casson, Fausto Milletari, Julian Viret, Eugene Vorontsov, SiQi Liu, Kristen Severson

Self-supervised learning (SSL) has emerged as a key technique for training networks that can generalize well to diverse tasks without task-specific supervision.

Position Self-Supervised Learning

Virchow: A Million-Slide Digital Pathology Foundation Model

no code implementations • 14 Sep 2023 • Eugene Vorontsov, Alican Bozkurt, Adam Casson, George Shaikovski, Michal Zelechowski, SiQi Liu, Kristen Severson, Eric Zimmermann, James Hall, Neil Tenenholtz, Nicolo Fusi, Philippe Mathieu, Alexander van Eck, Donghun Lee, Julian Viret, Eric Robert, Yi Kan Wang, Jeremy D. Kunz, Matthew C. H. Lee, Jan Bernhard, Ran A. Godrich, Gerard Oakley, Ewan Millar, Matthew Hanna, Juan Retamero, William A. Moye, Razik Yousfi, Christopher Kanan, David Klimstra, Brandon Rothrock, Thomas J. Fuchs

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 diagnosis and treatment of cancer.

Self-Supervised Learning whole slide images