TY - JOUR
T1 - GasHis-Transformer
T2 - A multi-scale visual transformer approach for gastric histopathological image detection
AU - Chen, Haoyuan
AU - Li, Chen
AU - Wang, Ge
AU - Li, Xiaoyan
AU - Mamunur Rahaman, Md
AU - Sun, Hongzan
AU - Hu, Weiming
AU - Li, Yixin
AU - Liu, Wanli
AU - Sun, Changhao
AU - Ai, Shiliang
AU - Grzegorzek, Marcin
N1 - Publisher Copyright:
© 2022 Elsevier Ltd
PY - 2022/10
Y1 - 2022/10
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85131457941&partnerID=8YFLogxK
UR - https://doi.org/10.1016/j.patcog.2022.108827
U2 - 10.1016/j.patcog.2022.108827
DO - 10.1016/j.patcog.2022.108827
M3 - Journal articles
AN - SCOPUS:85131457941
SN - 0031-3203
VL - 130
JO - Pattern Recognition
JF - Pattern Recognition
M1 - 108827
ER -
