GasHis-Transformer: A multi-scale visual transformer approach for gastric histopathological image detection
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.
- Publication:
-
Pattern Recognition
- Pub Date:
- October 2022
- DOI:
- 10.1016/j.patcog.2022.108827
- arXiv:
- arXiv:2104.14528
- Bibcode:
- 2022PatRe.13008827C
- Keywords:
-
- Gastric histropathological image;
- Multi-scale visual transformer;
- Image detection;
- Computer Science - Computer Vision and Pattern Recognition
- E-Print:
- Pattern Recognition Volume 130, October 2022, 108827