  In this paper, a multi-scale visual transformer model, referred as
GasHis-Transformer, is proposed for Gastric Histopathological Image Detection
(GHID), which enables the automatic global detection of gastric cancer images.
GasHis-Transformer model consists of two key modules designed to extract global
and local information using a position-encoded transformer model and a
convolutional neural network with local convolution, respectively. A publicly
available hematoxylin and eosin (H&E) stained gastric histopathological image
dataset is used in the experiment. Furthermore, a Dropconnect based lightweight
network is proposed to reduce the model size and training time of
GasHis-Transformer for clinical applications with improved confidence.
Moreover, a series of contrast and extended experiments verify the robustness,
extensibility and stability of GasHis-Transformer. In conclusion,
GasHis-Transformer demonstrates high global detection performance and shows its
significant potential in GHID task.
