Thomas Fuchs, Paige.AI cofounder and Chief Science Officer
A computational pathology startup has licensed technology from Memorial Sloan Kettering Cancer Center to apply machine learning algorithms to a library of pathology slides to advance oncology clinical decision support. In a phone interview, Paige.AI Cofounder and Chief Science Officer Thomas Fuchs said the funds raised from a $25 million Series A round will be used towards expanding the Memorial Sloan Kettering spinoff’s staff from five to 35 this year.
Next month, Paige.AI is moving into offices at Cornell Tech’s Tata Innovation Center in New York City, Fuchs said.
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Jim Breyer, Breyer Capital CEO, and Julian Robertson, who founded famed investment firm Tiger Management, led the round with other investors, according to Fuchs.
As part of the licensing deal with Memorial Sloan Kettering, the company gains exclusive rights to MSK’s access to a library of 25 million digitized pathology slides, one of the world’s largest tumor pathology archives. The company will use the library to develop machine learning applications starting with breast, prostate and other major cancers, according to a company press release.
The digitized slides also have de-identified pathologic and clinical annotations and anonymized genomic sequencing results.
Fuchs said Memorial Sloan Kettering’s distinction as a popular second opinion hospital has helped it build its sizeable tumor library. Paige.AI will leverage that reputation as it scales the company internationally. In addition to academic medical centers, Paige.AI also plans to partner with commercial labs and pharmaceutical companies.
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At Memorial Sloan Kettering, Fuchs is director of Computational Pathology in The Warren Alpert Center for Digital and Computational Pathology. He is also a professor at the Weill Cornell Graduate School of Medical Sciences, where he teaches machine learning. Fuchs cofounded the company with Dr. David Klimstra, who is the chairman of the Department of Pathology at MSK.
Prior to Paige.AI, Fuchs worked on the Mars Rover project and autonomous space exploration at NASA’s Jet Propulsion Laboratory. He drew parallels between algorithms used to identify terrain on Mars differentiating cancerous from benign tissues on slides.
“If you’re teaching a self-driving car on a closed course, anyone can label a tree or a sign so the system can recognize it,” said Fuchs in a company release. “But imagine the additional guidance that car would require to navigate New York City. The same is true in a specialized medical domain like oncology. You must have both massive data sets and specialists with decades of training to ensure that the computer models are up to difficult tasks.”
