It turns out interpreting biopsies isn’t entirely unlike mapping the rocky terrain of Mars. Thomas Fuchs, a former research technologist at NASA, is taking some of what he learned in training algorithms to navigate the Red Planet for the Mars Rover project and applying it to create cancer-detecting algorithms.
Fuchs (pictured) thinks his startup, Paige.AI, can leverage machine learning in pathology to increase accuracy, save pathologists’ time, and deliver better patient outcomes. Pathology, which involves reading slides from biopsies to evaluate tissue health, is a crucial step in the treatment of cancer patients. It influences everything to follow—from the treatments or therapy ordered, to the additional tests doctors decide to run.
New York-based Paige.AI is first focusing on training algorithms for prostate and breast cancer diagnosis. The company announced a $25 million Series A funding round led by Breyer Capital in February.
Using digitized slides, Paige.AI employs deep learning techniques to teach algorithms how to differentiate between slides of healthy tissue and those with abnormalities. By using algorithms to pull out only the slides most likely to be cancerous, Fuchs said, pathologists will be able to spend more of their time actually reasoning and analyzing the results, rather than sorting slides. This has the potential to cut the amount of time pathologists spend looking at slides in half, according to Fuchs.
To train machine learning algorithms to identify what type of cancer or cell abnormality is present, having access to quantity as well as quality of slides is crucial.
Fuchs, Paige.AI’s founder and chief scientific officer, is also the director of computational pathology in the Warren Alpert Center for Digital and Computational Pathology at Memorial Sloan Kettering Cancer Center. With approximately 30 percent of cases at the cancer center coming from outside, Fuchs said, the institution is already a popular resource for second opinions in pathology.
A recently inked agreement between Paige.AI and New York-based Memorial Sloan Kettering will give the startup exclusive access to the cancer center’s intellectual property in computational pathology, as well as its library of 25 million pathology slides for the next eight years. Fuchs said access to both Memorial Sloan Kettering’s library of slides as well as its world-class pathologists gives Paige.AI a leg up on the competition.
Fuchs ultimately hopes that by digitizing slides and perfecting his company’s algorithms, pathologists will be able to use the pattern recognition capabilities of Paige.AI to conduct something of an image search. Cross-referencing cancerous slides from a fresh biopsy with Paige.AI’s database of slides, the algorithms will make use of past cases where a patient presented with similar cell morphology.
Because raw images from the Memorial Sloan Kettering library include accompanying annotations and even genome sequencing tests, the image search could show the accompanying diagnosis, treatment, and even outcomes of patients who presented with similar cell morphology in the past—that’s the idea, at least.
Fuchs sees this potential to correlate images with sequencing data as Paige.AI’s real value for hospitals.
The genome sequencing data accompanying an image that matches a slide taken from a fresh biopsy could be used to predict how the tissue might mutate, meaning a hospital could potentially only need to do one expensive test in order to infer the results of the second.
But training the company’s deep neural networks takes a combination of