Health care startup today announced it has raised $25 million to continue its work in cancer diagnosis with help from computer vision trained with clinical imaging data. Datasets relating to treatment and genomics will also be included in the company’s deep learning models. Initial work by will center on the detection of breast, prostate, and other major cancers. also announced today it has signed an agreement with Memorial Sloan Kettering Cancer Center to gain access to its database of 25 million pathology slides.

Companies ranging from the startup Arterys to Google’s DeepMind are using computer vision trained by medical imaging to identify and diagnose diseases like cancer or degenerative eye conditions. Research highlighted in the journal Nature last year found that some computer vision can identify skin cancer as accurately as a panel of doctors. was founded by Dr. Thomas Fuchs, director of computational pathology at the Warren Alpert Center for Digital and Computational Pathology at Memorial Sloan Kettering. Fuchs said is working on technology to augment, not replace, pathologists.

“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,” Fuchs said in a statement shared with VentureBeat. “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.”

The $25 million series A funding round was led by Breyer Capital. emerges from stealth mode today but was incorporated last summer, a company spokesperson told VentureBeat. is based in New York City. currently has five employees but plans to use some of its funding to hire at least 20 additional employees.

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