Pretrained machine learning models could play a significant role in diagnosing non-melanoma skin cancer (NMSC) in regions with limited access to expert pathology services.
At the 2025 American Association for Cancer Research (AACR) Annual Meeting, the reseacher (Steven Song) explained that by harnessing the power of foundation models—large-scale, general-purpose machine learning models trained on extensive datasets—his research team demonstrated improved diagnostic accuracy compared to conventional approaches, particularly in resource-constrained environments.
In their study, the researchers evaluated the performance of 3 prominent foundation models against that of ResNet18 on digital pathology slides of skin tissue from the Bangladesh Vitamin E and Selenium Trial (BEST).
The dataset included 2130 high-resolution digital images from 553 biopsy samples. Of these, 1424 images represented various NMSC types—Bowen's disease, basal cell carcinoma, and invasive squamous cell carcinoma—while 706 images were of normal tissue.
The foundation models demonstrated strong performance, significantly surpassing that of ResNet18. ResNet18 achieved an accuracy of 80.5% in distinguishing between cancerous and non-cancerous tissue. In contrast, PRISM achieved an accuracy of 92.5%, UNI 91.3%, and Prov-GigaPath 90.8%.
To make deployment more feasible, they developed simplified versions of each foundation model that required less data processing. Encouragingly, these streamlined versions still performed well, achieving accuracies of 88.2% (PRISM), 86.5% (UNI), and 85.5% (Prov-GigaPath), again outperforming the ResNet18 baseline.
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