For decades pathologists have rendered their cancer diagnoses by performing a biopsy and examining a patient’s tumour sample under a microscope. Now, an increasing number of top-tier pathologists are adopting artificial intelligence techniques to improve their cancer diagnoses.
Paige.AI is a New York-based startup that fights cancer with AI. Launched last month, the company has an exclusive data license with the Memorial Sloan Kettering Cancer Center (MSK) — the largest cancer research institute in the US — which has a dataset of 25 million pathology cancer images (“slides”).
Typically, a pathologist must invest a significant amount of time examining a patient’s numerous tumour slides, each of which could be 10+ gigapixels when digitized at 40X magnification. Even the best pathologists can make a misdiagnosis, and it is not uncommon for professionals to disagree on diagnoses.
This is why computational pathology for cancer research has gained traction over the last ten years or so. The technology incorporates massive amounts of data, including pathology, radiology, clinical, molecular and lab tests; a computational model based on machine learning algorithms; and a visualized presentation interface that is understandable for both pathologists and patients.
“Computational pathology solutions will help streamline workflows in the future by screening situations that do not require a pathologist review,” said Jeroen van der Laak, Associate Professor at Radboud University Medical Center, in an interview with Philips Healthcare.
Dr. Thomas Fuchs is the Director of Computational Pathology at MSK and an early pioneer in the theoretical study of computational pathology, He has many years of experience in the development and application of advanced machine learning and computer vision techniques for tackling large-scale computational pathology challenges.
Last month Dr. Fuchs assumed an additional role as Founder and CEO of Paige.AI. He told Synced he believed the time was right to build Paige.AI because the requirements are all in place: scanners can deliver digital images with quality comparable to what pathologists see under the microscope; cancer centres scan some 40,000 pathology slides each month; and deep learning algorithms are well-suited for large-scale data.
Paige.AI’s technology is built on machine learning algorithms trained at petabyte-scale from tens of thousands of digital slides. Three models are utilized to solve different problems: convolutional neural networks for tasks such as image classification and segmentation, recurrent neural networks for information extraction from pathology reports, and generative adversarial networks to learn the underlying distribution of the unlabeled image data and to embed histology images in lower dimensional feature spaces.
Tech giants believe their frontier machine learning algorithms have huge a potential to revamp conventional diagnostic methodologies in the healthcare market, increasing accuracy and reducing costs. IBM has been using slides to train deep neural networks to detect tumours since 2016.
Google, meanwhile, has released research on how deep learning can be applied to computational pathology by creating an automated detection algorithm to improve pathologists’ workflow. Google successfully produced a tumor probability prediction heat map algorithm whose localization score (FROC) reached 89 percent, significantly outperforming pathologists’ average score of 73 percent.
“Companies like Microsoft and IBM are doing pathology, and in general, it is good for the whole field,” says Dr. Fuchs, who also warned that tech companies unfamiliar with the healthcare sector might have a hard time. “You have to really understand the variety of workflows and the community, and where and how AI can help. Besides, as far as I know, all previous papers published were based on a very tiny data set. Increasing the dataset from a few hundred images to hundreds of thousands of images can make a huge difference.”
In the short term, Paige.AI will provide pathologists with it’s “AI Module” application suite, equipped with a dedicated physical slide viewer that can integrate with any microscope. The AI module targets prostate, breast and lung cancers and can perform tasks such as cancer detection, quantification of tumour percentages, and survival analysis.
Paige.AI has already rolled out its product institution-wide at MSK, and aims to deliver disease-specific modules to pathologists later this year.
Paige.AI’s forte in algorithms and access to large-scale data attracted interest from Breyer Capital, which led a US$25 million Series A Funding Round for the company. Founder and CEO of Breyer Capital Jim Breyer, a venture capitalist renowned for his smart investments — most notably Facebook — wrote in a Medium blog, “Paige.AI is poised to become a powerhouse in computational pathology and an undisputed leader among thousands of healthcare AI competitors.”
Paige.AI certainly does not intend to limit its output to slide viewers — the company aspires to reshape the entire diagnosis and treatment paradigm. “With Paige.AI, we can, for example, based on hundreds of thousands of slides, come up with a better grade because you can actually correlate so many patients with the outcomes. Then we compute that correlation, and of course, change how you grade patients and how and which medications are prescribed,” says Dr. Fuchs.
Although the road ahead for Paige.AI is bound to be challenging, especially as the company is still at a very early stage in its development, Dr. Fuchs is determined to raise his company to the forefront in AI implementation in healthcare, and their research is likely to spark further technological breakthroughs for computational pathology.
Journalist: Tony Peng | Editor: Michael Sarazen
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