PAIGE brings clarity, confidence, and efficiency to pathology with ease.

Our Technology

At the heart of PAIGE are large-scale Machine Learning algorithms that are trained at petabyte-scale from tens of thousands of digital slides. We are developing novel deep learning algorithms based on convolutional and recurrent neural networks as well as generative models that are able to learn efficiently from an unprecedented wealth of visual and clinical data.

Digitized slides paired with clinical notes and machine learning algorithms will enable pathologists to reach diagnoses faster and more accurately.

Our Platform

We are building an Artificial Intelligence to fundamentally change clinical diagnosis and treatment of cancer. We are working on general and organ-specific modules which will allow PAIGE to fulfill a plethora of tasks, ranging from rapid stratification to tumor detection, segmentation, and prediction of treatment response and survival.

In addition, our slide viewer is vendor agnostic, device independent, fully integrated into the laboratory information systems, and can deliver PAIGE's AI modules to the pathologist. It allows for a seamless application in the clinical workflow.

In 2017 PAIGE's slide viewer was rolled out institution-wide at Memorial Sloan Kettering Cancer Center and is the single entry point for pathologists and cancer researchers. We are developing a series of disease-specific modules, which will be rolled out starting later in 2018.

Our Goals

01

Develop and 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.

02

Develop new treatment paradigms that build on the promise of computational pathology and integrate computational pathology with Electronic Health Records, genomic, and other data.

Our History

With the shift towards Digital Pathology in leading hospital centers over the last couple of years, there has been no better time for PAIGE to lead the revolution in Pathology.

2008

We published the first computational pathology paper and validated AI's level of accuracy on the survival of renal cancer patients

2011

We published the first review article about computational pathology

2013

Digitization of clinical pathology slides at MSKCC

2016

Build deeper learning models for cancer detection, segmentation, classification, and patient stratificiation

2018

Launch of Paige AI

2008

We published the first computational pathology paper and validated AI's level of accuracy on the survival of renal cancer patients

2011

We published the first review article about computational pathology

2013

Digitization of clinical pathology slides at MSKCC

2016

Build deeper learning models for cancer detection, segmentation, classification, and patient stratificiation

2018

Launch of Paige AI

Selected Publications

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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