Learning biologically relevant features in a pathology foundation model using sparse autoencoders

no code implementations • 15 Jul 2024 • Nhat Minh Le, Ciyue Shen, Neel Patel, Chintan Shah, Darpan Sanghavi, Blake Martin, Alfred Eng, Daniel Shenker, Harshith Padigela, Raymond Biju, Syed Ashar Javed, Jennifer Hipp, John Abel, Harsha Pokkalla, Sean Grullon, Dinkar Juyal

Previous works on interpretability for machine learning models on pathology images have revolved around methods such as attention value visualization and deriving human-interpretable features from model heatmaps.

PLUTO: Pathology-Universal Transformer

no code implementations • 13 May 2024 • Dinkar Juyal, Harshith Padigela, Chintan Shah, Daniel Shenker, Natalia Harguindeguy, Yi Liu, Blake Martin, Yibo Zhang, Michael Nercessian, Miles Markey, Isaac Finberg, Kelsey Luu, Daniel Borders, Syed Ashar Javed, Emma Krause, Raymond Biju, Aashish Sood, Allen Ma, Jackson Nyman, John Shamshoian, Guillaume Chhor, Darpan Sanghavi, Marc Thibault, Limin Yu, Fedaa Najdawi, Jennifer A. Hipp, Darren Fahy, Benjamin Glass, Eric Walk, John Abel, Harsha Pokkalla, Andrew H. Beck, Sean Grullon

Pathology is the study of microscopic inspection of tissue, and a pathology diagnosis is often the medical gold standard to diagnose disease.

Instance Segmentation Semantic Segmentation

Ontologizing Health Systems Data at Scale: Making Translational Discovery a Reality

1 code implementation • 10 Sep 2022 • Tiffany J. Callahan, Adrianne L. Stefanski, Jordan M. Wyrwa, Chenjie Zeng, Anna Ostropolets, Juan M. Banda, William A. Baumgartner Jr., Richard D. Boyce, Elena Casiraghi, Ben D. Coleman, Janine H. Collins, Sara J. Deakyne-Davies, James A. Feinstein, Melissa A. Haendel, Asiyah Y. Lin, Blake Martin, Nicolas A. Matentzoglu, Daniella Meeker, Justin Reese, Jessica Sinclair, Sanya B. Taneja, Katy E. Trinkley, Nicole A. Vasilevsky, Andrew Williams, Xingman A. Zhang, Joshua C. Denny, Peter N. Robinson, Patrick Ryan, George Hripcsak, Tellen D. Bennett, Lawrence E. Hunter, Michael G. Kahn

Background: Common data models solve many challenges of standardizing electronic health record (EHR) data, but are unable to semantically integrate all the resources needed for deep phenotyping.