Waterloo researchers are accelerating the cancer diagnosis process via artificial intelligence (AI)

Wednesday, July 21, 2021

By Mayuri Punithan

For many pathologists, diagnosing cancer is one stressful and complicated process.

Typically, these medical professionals are alone in their office, examining a biopsy sample through a microscope. Only in major research hospitals may they have the privilege of consulting with other colleagues for a second opinion. In only very doubtful cases, they may request a teleconsultation. Although printed or digital atlases containing thousands of sample cancer images approved by professionals such as the World Health Organization may be helpful, they may not be up-to-date making the diagnosis process time consuming and inefficient.

Unfortunately, not every medical professional has this resource, especially in remote locations across the world such as Latin America and Africa. Pathologists are unable to make proper assessments in these countries with poor health conditions and greater need for high quality medical resources.

Now, Hamid Tizhoosh, Professor of Systems Design Engineering at the University of Waterloo wants to change that. As Director of the Laboratory for Knowledge Inference in Medical Imaging Analysis (Kimia Lab), he is leading a joint project with the International Collaboration for Cancer Classification and Research (IC3R). This new consortium is co-ordinating research on tumour classification and cancer diagnosis. An international collaboration with partners in France, Belgium, Italy, the United States, Australia, Japan, and Singapore, the Kimia Lab is the only Canadian partner. IC3R’s main mission is to provide a faster and more accurate way to diagnose and treat numerous diseases and to establish new quality assurance through information extraction.

Recently, IC3R have started to collaborate on creating a search engine that contains WHO archives of breast cancer samples. Professor Tizhoosh says it contains 39 different types of common and rare abnormalities, some that are cancer and some that are benign. The user scans the biopsy sample, and the search engine will identify the closest sample picture, quantifying the tissue similarity. Within seconds, pathologists can find the most likely diagnosis, thus accelerating the diagnosis process, especially for those in remote places.

How did this collaboration evolve? While attending the annual meeting of the United States and Canadian Academy of Pathology (USCAP), the largest pathology conference in America, Professor Tizhoosh demonstrated a search engine to fellow researchers from America. Through discussion, they realized they had a lot of “synergy that they could explore together.”

Soon after, IC3R began to collect images from Grand River Hospital for external validation while simultaneously receiving support from renowned universities such as the University of Michigan and the University of British Columbia. After launching the invention, Kimia Lab, which combines medical archives and machine learning for medical diagnosis and analysis, started to test and use it. The pilot project is currently focused on breast cancer diagnosis, but researchers are open to exploring diverse types of cancers in the near future.