ArticleTechnology and health care : official journal of the European Society for Engineering and Medicine2022
HER2-ResNet: A HER2 classification method based on deep residual network.
Article in Technology and health care : official journal of the European Society for Engineering and Medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed, 13 citations in OpenAlex.
- Classification of human epidermal growth factor receptor 2 expression in cancerous breast tissue through artificial intelligenceBiomedica : revista del Instituto Nacional de Salud · 2025Article
- Enhancing glioma immunohistochemical image classification through color deconvolution-aware prior guidance.PloS one · 2025Article
- Article
- A glimpse into the future: Integrating artificial intelligence for precision HER2-positive breast cancer management.iMetaOmics · 2024Review
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Authors and funding
5 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundHER2 gene expression is one of the main reference indicators for breast cancer detection and treatment, and it is also an important target for tumor targeted therapy drug selection. Therefore, the correct detection and evaluation of HER2 gene expression has important value for clinical treatment of breast cancer.
objectiveThe study goal is to better classify HER2 images.
methodsFor general convolution neural network, with the increase of network layers, over fitting phenomenon is often very serious, which requires setting the value of random descent ratio, and parameter adjustment is often time-consuming and laborious, so this paper uses residual network, with the increase of network layer, the accuracy will not be reduced.
resultsIn this paper, a HER2 image classification algorithm based on improved residual network is proposed. Experimental results show that the proposed HER2 network has high accuracy in breast cancer assessment.
conclusionTaking HER2 images in Stanford University database as experimental data, the accuracy of HER2 image automatic classification is improved through experiments. This method will help to reduce the detection intensity and improve the accuracy of HER2 image classification.
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Registered trials
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