Our aspiration is to build the best AI in clinical medicine

Our Strategy

Our short term plan is to deliver a series of AI disease modules that allow pathologists to improve the scalability of their work, enabling them to provide better care, at lower cost. Our medium to long-term plan is to develop new treatment paradigms that integrate computational pathology with electronic health records, genomic and other clinical data.

Our Products

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

PAIGE Guidance Engine​

We are working on general and organ-specific modules. PAIGE will be able to fulfill tasks ranging from rapid stratification to tumor detection, segmentation, prediction of treatment response and survival. ​

PAIGE View​

We are developing tools to facilitate broad adoption. For example, our Universal Slide viewer is vendor agnostic, and device independent. In 2017, PAIGE’s slide viewer was rolled out 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 value our ability to 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.
Thomas J. Fuchs, Peter J. Wild, Holger Moch and Joachim M. Buhmann.

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, Judy Sarungbam, Hassan Muhammad, Ed Reznik, Satish K. Tickoo 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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Partnerships and People

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

Meet The Team