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