  For deep learning methods applied to the diagnosis of gastric cancer
intelligently, existing methods concentrate more on Convolutional Neural
Networks (CNN) but no approaches are available using Visual Transformer (VT).
VT's efficient and stable deep learning models with the most recent application
in the field of computer vision, which is capable of improving the recognition
of global information in images. In this paper, a multi-scale visual
transformer model (GasHis-Transformer) is proposed for a gastric histopathology
image classification (GHIC) task, which enables the automatic classification of
gastric histological images of abnormal and normal cancer by obtained by
optical microscopy to facilitate the medical work of histopathologists. This
GasHis-Transformer model is built on two fundamental modules, including a
global information module (GIM) and a local information module (LIM). In the
experiment, an open source hematoxylin and eosin (H&E) stained gastric
histopathology dataset with 280 abnormal or normal images are divided into
training, validation, and test sets at a ratio of 1:1:2 first. Then,
GasHis-Transformer obtains precision, recall, F1-score, and accuracy on the
testing set of 98.0%, 100.0%, 96.0%, and 98.0%. Furthermore, a contrast
experiment also tests the generalization ability of the proposed
GatHis-Transformer model with a lymphoma image dataset including 374 images and
a breast cancer dataset including 1390 images in two extended experiments and
achieves an accuracy of 83.9% and 89.4%, respectively. Finally,
GasHis-Transformer model demonstrates high classification performance and shows
its effectiveness and enormous potential in GHIC tasks.
