Deconv-transformer (DecT): A histopathological image classification model for breast cancer based on color deconvolution and transformer architecture
Histopathological image recognition of breast cancer is an onerous task. Although many deep learning models have achieved good classification results on histopathological image classification tasks, these models do not take full advantage of the staining properties of histopathological images. In th...
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| Published in | Information sciences Vol. 608; pp. 1093 - 1112 |
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| Main Authors | , , , , , , |
| Format | Journal Article |
| Language | English |
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Elsevier Inc
01.08.2022
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| Abstract | Histopathological image recognition of breast cancer is an onerous task. Although many deep learning models have achieved good classification results on histopathological image classification tasks, these models do not take full advantage of the staining properties of histopathological images. In this paper, we propose a novel Deconv-Transformer (DecT) network model, which incorporates the color deconvolution in the form of convolution layers. This model uses a self-attention mechanism to match the independent properties of the HED channel information obtained by the color deconvolution. It also uses a method similar to the residual connection to fuse the information of both RGB and HED color space images, which can compensate for the information loss in the process of transferring RGB images to HED images. The training process of the DecT model is divided into two stages so that the parameters of the deconvolution layer can be better adapted to different types of histopathological images. We use the color jitter in the image data augmentation process to reduce the overfitting in the model training process. The DecT model achieves an average accuracy of 93.02% and F1-score of 0.9389 on BreakHis dataset, and an average accuracy of 79.06% and 81.36% on BACH and UC datasets. |
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| AbstractList | Histopathological image recognition of breast cancer is an onerous task. Although many deep learning models have achieved good classification results on histopathological image classification tasks, these models do not take full advantage of the staining properties of histopathological images. In this paper, we propose a novel Deconv-Transformer (DecT) network model, which incorporates the color deconvolution in the form of convolution layers. This model uses a self-attention mechanism to match the independent properties of the HED channel information obtained by the color deconvolution. It also uses a method similar to the residual connection to fuse the information of both RGB and HED color space images, which can compensate for the information loss in the process of transferring RGB images to HED images. The training process of the DecT model is divided into two stages so that the parameters of the deconvolution layer can be better adapted to different types of histopathological images. We use the color jitter in the image data augmentation process to reduce the overfitting in the model training process. The DecT model achieves an average accuracy of 93.02% and F1-score of 0.9389 on BreakHis dataset, and an average accuracy of 79.06% and 81.36% on BACH and UC datasets. |
| Author | He, Zhu Chen, Hong Lin, Mingwei Alenezi, Fayadh Yao, Zhiqiang Xu, Zeshui Alhudhaif, Adi |
| Author_xml | – sequence: 1 givenname: Zhu surname: He fullname: He, Zhu organization: College of Computer and Cyber Security, Fujian Normal University, Fuzhou, Fujian 350117, China – sequence: 2 givenname: Mingwei surname: Lin fullname: Lin, Mingwei email: linmwcs@163.com organization: College of Computer and Cyber Security, Fujian Normal University, Fuzhou, Fujian 350117, China – sequence: 3 givenname: Zeshui surname: Xu fullname: Xu, Zeshui organization: Business School, Sichuan University, Chengdu, Sichuan 610064, China – sequence: 4 givenname: Zhiqiang surname: Yao fullname: Yao, Zhiqiang organization: College of Computer and Cyber Security, Fujian Normal University, Fuzhou, Fujian 350117, China – sequence: 5 givenname: Hong surname: Chen fullname: Chen, Hong organization: School of Mathematics and Statistics, Fujian Normal University, Fuzhou, Fujian 350117, China – sequence: 6 givenname: Adi surname: Alhudhaif fullname: Alhudhaif, Adi organization: Department of Computer Science, College of Computer Engineering and Sciences in Al-kharj, Prince Sattam Bin Abdulaziz University, P.O. Box 151, Al-Kharj 11942, Saudi Arabia – sequence: 7 givenname: Fayadh surname: Alenezi fullname: Alenezi, Fayadh organization: Department of Electrical Engineering, College of Engineering, Jouf University, Saudi Arabia |
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| SubjectTerms | Breast cancer Color deconvolution Color space Deep learning Histopathological image |
| Title | Deconv-transformer (DecT): A histopathological image classification model for breast cancer based on color deconvolution and transformer architecture |
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