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• W4308571828 abstract "Abstract Histological grade is one of the most powerful prognostic factors for breast cancer and impacts treatment decisions. However, a label-free and automated classification system for histological grading of breast tumors has not yet been developed. In this study, we employed label-free multiphoton microscopy (MPM) to acquire subcellular-resolution images of unstained breast cancer tissues. Subsequently, a deep-learning algorithm based on the generative adversarial network (GAN) was introduced to learn a representation using only MPM images without the histological grade information. Furthermore, to obtain abundant image information and determine the detailed differences between MPM images of different grades, a multiple-feature discriminator network based on the GAN was leveraged to learn the multi-scale spatial features of MPM images through unlabeled data. The experimental results showed that the classification accuracies for tumors of grades 1, 2, and 3 were 92.4%, 88.6%, and 89.0%, respectively. Our results suggest that the fusion of multiphoton microscopy and the GAN-based deep learning algorithm can be used as a fast and powerful clinical tool for the computer-aided intelligent pathological diagnosis of breast cancer." @default.
• W4308571828 created "2022-11-12" @default.
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• W4308571828 date "2022-11-29" @default.
• W4308571828 modified "2023-10-16" @default.
• W4308571828 title "Automated classification of breast cancer histologic grade using multiphoton microscopy and generative adversarial networks" @default.
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• W4308571828 doi "https://doi.org/10.1088/1361-6463/aca104" @default.
• W4308571828 hasPublicationYear "2022" @default.
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