Researchers in the laboratory of Xinming Huang who are developing a tool to analyze skin images for signs of skin cancer have finished third in an international artificial intelligence (AI) competition aimed at improving early diagnosis of melanoma, a potentially lethal type of skin cancer.
The team’s model combined two types of AI to finish third in the competition, which was organized by the International Skin Imaging Collaboration (ISIC). The challenge drew 181 teams of academic and corporate researchers.
“Melanoma can be difficult for primary care doctors, especially those who are not skin experts, to identify in its early stages,” says Huang, a professor in the Department of Electrical and Computer Engineering. “AI has the potential to help those physicians spot patients who need to be referred to specialists.”
TwinSight, the model under development in Huang’s lab, builds on research that PhD student Antony Garcia Gonzalez and Huang reported in 2025 in the peer-reviewed journal Cancers. TwinSight analyzes the visual characteristics of a lesion using both a convolutional neural network and a vision transformer, two different deep-learning computer programs that examine complex patterns in data.
The TwinSight model seeks to address a critical problem: Melanoma, especially in its early stages, can be difficult to visually distinguish from benign skin moles, and delays in diagnosis and treatment raise the risk that the cancer will spread.
Primary care physicians will often refer patients with suspicious lesions to dermatologists, who will typically examine the lesions using a dermoscope, a magnifying tool, and perform a skin biopsy to confirm the diagnosis.
