A research program focused on early detection and epidemiological research on skin cancer. We have been developing artificial intelligence and traditional machine learning methods for detecting skin cancer from photographs taken by digital cameras and smartphones. New devices and technologies are also being developed to compliment the advanced software techniques. The melanoma probe (pictured above) is based on the phenomenon of optical polarization, and was developed to help provide a new source of rapid skin lesion information. The research team also investigates the prognosis and risk factors of melanoma, exploring new ways to extract and analyze information from both biopsied samples and patients in vivo.
Dermatology and Skin Sciences in Cancer
Dr. Tim Lee, PhD
Dr. Sunil Kalia, MD, MHSc, FAAD, FRCPC
Dr. Lioudmila Tchvialeva, PhD
Dr. Yuheng Wang, PhD
Daniel Louie, MSc
Polarization sensitive optical coherence tomography (PS-OCT)
Optical coherence tomography is an imaging method best described as “ultrasound but with light”. It is a non-invasive imaging technique that allows for rapid subsurface imaging of the skin, allowing us to reveal key health properties.
Polarization Speckle Analysis
Polarization speckle is a noise-like interference pattern created when a skin lesion is illuminated by laser light.
In this project, we develop a handheld and easy-to-operate optical probe for detecting melanoma. Delivery of the technology is aimed at non-dermatologist healthcare providers, improving their diagnostic accuracy and reducing the number of referrals to the specialists.
Automated skin cancer detection systems
Computer-aided systems for melanoma - The objectives of this project are to develop automated computer systems to help interpret complex dermoscopic images with the ultimate aim of distributing the technique to non-dermatologist healthcare providers and, maybe, even patients.
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