GasHis-Transformer: A Multi-scale Visual Transformer Approach for Gastric Histopathological Image Detection
This work addresses gastric cancer diagnosis for medical applications, but it appears incremental as it combines existing transformer and CNN techniques for a specific domain.
The authors tackled gastric cancer detection from histopathological images by proposing GasHis-Transformer, a multi-scale visual transformer model that achieved high global detection performance, though no specific numerical results were provided in the 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.