GasHis-Transformer: A Multi-scale Visual Transformer Approach for Gastric Histopathological Image Detection
Autor: | Haoyuan Chen, Chen Li, Ge Wang, Xiaoyan Li, Md Mamunur Rahaman, Hongzan Sun, Weiming Hu, Yixin Li, Wanli Liu, Changhao Sun, Shiliang Ai, Marcin Grzegorzek |
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Rok vydání: | 2021 |
Předmět: | |
DOI: | 10.48550/arxiv.2104.14528 |
Popis: | 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. |
Databáze: | OpenAIRE |
Externí odkaz: |
Abstrakt: | 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. |
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DOI: | 10.48550/arxiv.2104.14528 |