GasHis-Transformer: A multi-scale visual transformer approach for gastric histopathological image detection

Haoyuan Chen, Chen Li*, Ge Wang, Xiaoyan Li, Md Mamunur Rahaman, Hongzan Sun, Weiming Hu, Yixin Li, Wanli Liu, Changhao Sun, Shiliang Ai, Marcin Grzegorzek

*Corresponding author for this work
15 Citations (Scopus)


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.

Original languageEnglish
Article number108827
JournalPattern Recognition
Publication statusPublished - 10.2022

Research Areas and Centers

  • Centers: Center for Artificial Intelligence Luebeck (ZKIL)


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