  Gastric cancer is one of the most common cancers, which ranks third among the
leading causes of cancer death. Biopsy of gastric mucosa is a standard
procedure in gastric cancer screening test. However, manual pathological
inspection is labor-intensive and time-consuming. Besides, it is challenging
for an automated algorithm to locate the small lesion regions in the gigapixel
whole-slide image and make the decision correctly.To tackle these issues, we
collected large-scale whole-slide image dataset with detailed lesion region
annotation and designed a whole-slide image analyzing framework consisting of 3
networks which could not only determine the screening result but also present
the suspicious areas to the pathologist for reference. Experiments demonstrated
that our proposed framework achieves sensitivity of 97.05% and specificity of
92.72% in screening task and Dice coefficient of 0.8331 in segmentation task.
Furthermore, we tested our best model in real-world scenario on 10,315
whole-slide images collected from 4 medical centers.
