Our aspiration is to build the best AI in clinical medicine

Our Strategy

Our short term plan is to deliver a series of AI modules that allow pathologists to improve the scalability of their work, and thus provide better care, at lower cost. Our medium to long-term plan is to develop prognostic tools that integrate computational pathology with electronic health records, genomic and other clinical data to provide clinicians with layers of information to better optimize patient care.

Our Products

Powered by robust machine learning models, specifically designed for computational pathology.

Paige Modules

We are working on general and organ-specific modules to fulfill tasks including rapid diagnostic stratification, cancer detection, tumor segmentation, prediction of treatment response and overall survival. ​

Paige View​

We are developing tools to facilitate broad adoption. Our flexible slide viewer is vendor agnostic, and device independent. In 2017, Paige's slide viewer was deployed institution-wide at Memorial Sloan Kettering Cancer Center for pathologists and cancer researchers.

HPC Infrastructure: AI at Scale

With our AI-Ready Infrastructure's processing power of 10 petabytes, we can operationalize our data and algorithms at large scale. Our techniques have been validated against the world's largest datasets in pathology.

Research: Publications

We are proud to actively contribute to medical literature and advancements in this field.

Computational Pathology Analysis of Tissue Microarrays Predicts Survival of Renal Clear Cell Carcinoma Patients.
Thomas J. Fuchs, Peter J. Wild, Holger Moch and Joachim M. Buhmann.

Proceedings of the international conference on Medical Image Computing and Computer-Assisted Intervention MICCAI, vol. 5242, p. 1-8, Lecture Notes in Computer Science, Springer-Verlag, ISBN 978-3-540-85989-5, 2008

Computational Pathology: Challenges and Promises for Tissue Analysis.
Thomas J. Fuchs and Joachim M. Buhmann.

Computerized Medical Imaging and Graphics, vol. 35, 7–8, p. 515-530, 2011

Mitochondria-based Renal Cell Carcinoma Subtyping: Learning from Deep vs. Flat Feature Representations.
Peter J. Schüffler, Judy Sarungbam, Hassan Muhammad, Ed Reznik, Satish K. Tickoo and Thomas J. Fuchs.

Proceedings of the 1st Machine Learning for Healthcare Conference, Machine Learning for Healthcare, vol. 56, p. 191-208, Proceedings of Machine Learning Research, PMLR, 2016

Computational Pathology.
Peter J. Schüffler, Qing Zhong, Peter J. Wild and Thomas J. Fuchs.

In: Johannes Haybäck (ed.) Mechanisms of Molecular Carcinogenesis - Volume 2, 1st ed. 2017 edition, Springer, ISBN 3-319-53660-5, 21. Jun. 2017

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We foster national and global partnerships with academic medical centers, clinical labs, and pharmaceutical companies to advance the field of computational pathology and improve how cancer is diagnosed and treated.​

Meet The Team