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• W4238087693 abstract "Fine-grained classification and grading of breast cancer (BC) histopathological images are of great value in clinical application. However, automatic classification and grading of BC histopathological images are complicated by (1) small inter-class variance and large intra-class variance exist in BC histopathological images, and (2) features extracted from similar histopathological images with different magnification are quite different. To address these issues, an improved deep convolution neural network model is proposed and the procedure can be divided into three main stages. Firstly, in the representation learning process, multi-class recognition task and verification task of image pair are combined. Secondly, in the feature extraction process, a prior knowledge is built, which is “the variances in feature outputs between different subclasses is relatively large while the variance between the same subclass is small.” Additionally, the prior information that histopathological images with different magnification belong to the same subclass are embedded in the feature extraction process, which contributes to less sensitive with image magnification. The experimental results based on three different histopathological image datasets show that the performance of the proposed method is better than state of the art, with better robustness and generalization ability." @default.
• W4238087693 created "2022-05-12" @default.
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• W4238087693 date "2018-12-07" @default.
• W4238087693 modified "2023-10-16" @default.
• W4238087693 title "Multi-task deep learning for fine-grained classification and grading in breast cancer histopathological images" @default.
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• W4238087693 doi "https://doi.org/10.1007/s11042-018-6970-9" @default.
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