AI in Medicine Lab
At the interface of computation, engineering and medicine.
Research Highlights

Learning generalizable AI models for multi-center histopathology image classification

Histopathology—the study of tissue samples under a microscope—is a key method for diagnosing cancer. Pathologists examine slides to identify cancerous cells, but the process is time-consuming and prone to variability, especially with differences in tissue preparation and slide staining.

AI-based histopathology image analysis reveals a distinct subset of endometrial cancers

This research focuses on improving the classification of endometrial cancer (EC), a cancer that affects the lining of the uterus. Endometrial Cancer (EC) has four molecular subtypes, each with different implications for prognosis and treatment.

VOLTA: an enVironment-aware cOntrastive ceLl represenTation leArning for histopathology

This research introduces a new machine learning framework called VOLTA (enVironment-aware cOntrastive cell represenTation learning), designed to improve how cells are identified in histopathology images, which are critical for diagnosing cancer.

"AI is revolutionizing cancer diagnosis with new methods making it easier for doctors to examine tissues and identify cancerous cells."

Who We Are & What We Do

The AIM Lab (Artificial Intelligence in Medicine Lab) at the University of British Columbia, is dedicated to advancing healthcare through the power of AI. Our team of researchers, engineers, and healthcare professionals work together to develop cutting-edge solutions that address some of the most pressing challenges in medicine. From improving diagnostic accuracy to enhancing treatment outcomes, we combine innovative AI technologies with deep domain expertise to transform healthcare delivery. Whether you're a researcher, healthcare provider, or tech innovator, AIM Lab is your partner in shaping the future of medicine.

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