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.

翻译：在本文中,提出了一种称为GasHis-Transext的多级视觉变压器模型(GHID)用于气态病理图象探测(GHID),该模型可以自动在全球范围探测胃癌图象。GasHis-Transed模型由两个关键模块组成,分别用定位编码变压器模型和与本地变异的进化神经网络来提取全球和地方信息。实验中使用了一种公开提供的血氧素和eosin(H&E)腐蚀气态病理学图象数据集。此外,还提出了一种基于滴接的轻量网络,以减少GasHis-Trans的模型规模和培训时间,以便更有信心地进行临床应用。此外,一系列对比和扩展实验还验证了GasHis-Transtraverstex的坚固性、可扩展性和稳定性。最后,GasHis-Transexion展示了高全球探测性,并展示了其在GHID任务中的巨大潜力。