Paige for Clinical
Paige for Life Sciences
Identify actionable information from pathologic, genomic, and clinical data upon which treatment strategies and patient subpopulations may be identified.
- 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
We are proud to actively contribute to medical literature and advancements in this field.
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
Computerized Medical Imaging and Graphics, vol. 35, 7–8, p. 515-530, 2011
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
In: Johannes Haybäck (ed.) Mechanisms of Molecular Carcinogenesis - Volume 2, 1st ed. 2017 edition, Springer, ISBN 3-319-53660-5, 21. Jun. 2017
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