Figure 1 from AI-assisted Diagnosis of Nonmelanoma Skin Cancer in Resource-Limited Settings
Year: 2025
Type: other
Abstract: Overview of generating whole-slide embeddings using FMs. Each WSI is split into 256 × 256 px tiles, and tiles with visible tissue are selected for further processing. Tile features are extracted by a tile encoder, tile embeddings for a WSI are aggregated into a slide embedding, and NMSC is classified using the slide-level embedding. Tile aggregation methods include ABMIL, FM-specific aggregators, or GAP. Classification is performed using LR, XGBoost, or a shallow MLP. ViT, vision transformer.
Language: English
Topic: AI in cancer detection
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
OpenAlex ID: W4411907373