Abstract
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 language | English |
|---|---|
| Article number | 108827 |
| Journal | Pattern Recognition |
| Volume | 130 |
| ISSN | 0031-3203 |
| DOIs | |
| Publication status | Published - 10.2022 |
Funding
This work is supported by National Natural Science Foundation of China (No. 61806047 ). We thank Miss Zixian Li and Mr. Guoxian Li for their important discussion. We also thank Mr. Jinghua Zhang for his contribution to the revision of this paper. There is no conflict of interest in this paper.
Research Areas and Centers
- Centers: Center for Artificial Intelligence Luebeck (ZKIL)
