Artificial Intelligence Search Technology Will be Used to Help Modernize US Federal Pathology Facility
Technology developed by Canadian researchers has been adopted by the Joint Pathology Center (JPC), which has the world’s largest collection of preserved human tissue samples. The v=center will use an artificial intelligence (AI) search engine to index and search its digital archive as part of a modernization effort. The image search engine was designed by researchers at the Laboratory for Knowledge Inference in Medical Image Analysis (Kimia Lab) at the University of Waterloo.
The Joint Pathology Center (JPC), which has the world’s largest collection of preserved human tissue samples, will use an artificial intelligence (AI) search engine to index and search its digital archive as part of a modernization effort.
The image search engine was designed by researchers at the Laboratory for Knowledge Inference in Medical Image Analysis (KIMIA Lab) at the University Waterloo. It is commercialized under the name Lagotto (TM).
The image retrieval technology, with the scientific name Yottixel, allows pathologists, researchers and educators to search large archives of digital images to tap into rich diagnostic data.
Yottixel will be used to enhance biomedical research for infectious diseases and cancer, enabling easier data sharing to facilitate collaboration and medical advances.
The JPC is the leading pathology reference centre for the US federal government and part of the US Defense Health Agency. In the last century, it has collected more than 55 million glass slides and 35 million tissue block samples. Its data spans every major epidemic and pandemic, and was used to sequence the Spanish flu virus of 1918. It is expected that the modernization also helps to better understand and fight the COVID-19 pandemic.
“We are delighted to see that our algorithms are about to explore the world’s largest digital archive of biopsy samples,” Professor Hamid Tizhoosh, the Director of Kimia Lab, says, “we will continue to design and commercialize novel AI solutions for the medical field. The opportunity comes with unprecedented challenges that need fresh ideas and established competency to fully exploit the big data for the diagnostic imaging, precision medicine, and drug discovery of the future.”
Researchers at Waterloo have obtained promising diagnostic results using their AI search technology to match digital images of tissue samples in suspected cancer cases with known cases in a database. In a paper published earlier this year, a validation project led by Kimia Lab achieved accurate diagnoses for 32 kinds of cancer in 25 organs and body parts.
“We showed Yottixel can get incredibly encouraging results if it has access to a large archive,” said Hamid Tizhoosh. “Image search is undoubtedly a platform to intelligently exploit big image data by exploring medical repositories.”
About Kimia Lab
The Laboratory for Knowledge Inference in Medical Image Analysis (Kimia Lab) is a research group hosted at the Faculty of Engineering, University of Waterloo, On, Canada. Kimia Lab, established in 2013, is a member of Waterloo Artificial Intelligence Institute and conducts research at the forefront of mass image data in medical archives using machine learning schemes. The lab trains graduate and undergraduate students and annually hosts international visiting scholars. Professor Hamid Tizhoosh, Kimia Lab's director, is an expert in medical image analysis who has been working on different aspects of artificial intelligence since 1993. He is a faculty affiliate to the Vector Institute, Toronto, Canada.