Paige, a New York-based company, is working to revolutionize the diagnosis and treatment of cancer by providing pathologists, clinicians and researchers with insights drawn from decades of data diagnosed by world experts in cancer care. Paige uses large-scale machine learning algorithms that are trained at petabyte-scale from tens of thousands of digital slides. They 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.
Paige derive their name from four different concepts that represent the company as a whole:
- Pathology: Pathology is the cornerstone of cancer diagnoses. The field is on the cusp of a revolution towards digital, augmented clinical analysis. Paige aims to leverage cutting-edge AI and a vast, proprietary dataset to provide powerful new insights to pathologists, researchers, and pharmaceutical development teams.
- Artificial Intelligence: Artificial Intelligence is at Paige’s core. Their experts have a decade of experience in building large scale machine learning systems for computational pathology. Paige’s algorithms are trained with diagnoses from the world’s foremost cancer experts and hundreds of thousands of digital slides.
- Guidance: Paige’s AI suite is designed to guide pathologists, clinicians and researchers via their robust clinical decision support systems, providing clinical experts with the potential to gain efficiencies and reproducibility for their data analyses.
- Engine: Paige’s AIRI infrastructure, with GPU performance of more than 10 petaFLOPS, allows us to train our models at an unprecedented scale. Our unique, scanner-neutral slide viewer provides a sleek user interface for every-day digital pathology, and is designed to clearly overlay AI results for Pathologists.
Recently named as one of CB Insights most promising digital health startups, Paige’s 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 a lower cost. Their 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. For more information on Paige, please visit their website.
