GasHis-Transformer: A Multi-scale Visual Transformer Approach for Gastric Histopathological Image Detection

TL;DR

GasHis-Transformer is a multi-scale visual transformer model designed for automatic detection of gastric cancer in histopathological images, combining global and local feature extraction with a lightweight network for clinical use.

Contribution

The paper introduces GasHis-Transformer, a novel multi-scale transformer model with a lightweight variant, enhancing gastric histopathological image detection efficiency and robustness.

Findings

High detection accuracy on gastric histopathological images

Reduced model size and training time with Dropconnect

Robustness and stability demonstrated through extensive experiments

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.