A hybrid 2-stage vision transformer for AI-assisted 5 class pathologic diagnosis of gastric endoscopic biopsies

Gastric endoscopic screening is an effective way to decide appropriate gastric cancer (GC) treatment at an early stage, reducing GC-associated mortality rate. Although artificial intelligence (AI) has brought a great promise to assist pathologist to screen digitalized whole slide images, automatic classification systems for guiding proper GC treatment based on clinical guideline are still lacking. Here, we propose an AI system classifying 5 classes of GC histology, which can be perfectly matched to general treatment guidance. The AI system, mimicking the way pathologist understand slides through multi-scale self-attention mechanism using a 2-stage Vision Transformer, demonstrates clinical capability by achieving diagnostic sensitivity of above 85% for both internal and external cohort analysis. Furthermore, AI-assisted pathologists showed significantly improved diagnostic sensitivity by 10% within 18% saved screening time compared to human pathologists. Our AI system has a great potential for providing presumptive pathologic opinion for deciding proper treatment for early GC patients.