Enabling pathologists to make accurate digital diagnoses

Paige for Clinical

The Paige Platform

A better way to go digital

The Paige Platform offers secure, scalable digital slide storage and FullFocus™, an intuitive, FDA cleared, CE marked viewer for digital images. The platform is designed for pathologists to further leverage computational pathology products as they become available for clinical use.

Computational Pathology Products*

A portfolio of organ-specific digital diagnostics and biomarkers to optimize efficiency, reduce uncertainty and provide new layers of information from digital slides.

Paige for Life Sciences

Identify actionable information from pathologic, genomic, and clinical data upon which treatment strategies and patient subpopulations may be identified.

Leverage Paige's:

  • Access to proprietary data to quickly generate and test digital biomarker hypotheses while de-risking investment of time and money
  • SaaS-based deployment of digital biomarkers to identify appropriate patients for therapies and clinical trials while maximizing access
  • Clinical-grade cancer detection and characterization solutions to confidently assess cancer status in neo-, peri-, and adjuvant treatment settings

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