  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,
existing AI systems are limited in fine-grained cancer subclassifications and
have little usability in planning cancer treatment. We propose a practical AI
system that enables five subclassifications of GC pathology, which can be
directly matched to general GC treatment guidance. The AI system is designed to
efficiently differentiate multi-classes of GC through multi-scale
self-attention mechanism using 2-stage hybrid Vision Transformer (ViT)
networks, by mimicking the way how human pathologists understand histology. The
AI system demonstrates reliable diagnostic performance by achieving
class-average sensitivity of above 0.85 on a total of 1,212 slides from
multicentric cohort. Furthermore, AI-assisted pathologists show significantly
improved diagnostic sensitivity by 12% in addition to 18% reduced screening
time compared to human pathologists. Our results demonstrate that AI-assisted
gastric endoscopic screening has a great potential for providing presumptive
pathologic opinion and appropriate cancer treatment of gastric cancer in
practical clinical settings.
