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• W4308661657 abstract "Accurate and robust human immune system assessment through white blood cell evaluation require computer-aided tools with pathologist-level accuracy. This work presents a multi-attention leukocytes subtype classification method by leveraging fine-grained and spatial locality attributes of white blood cell. The proposed framework comprises three main components: texture-aware/attention map generation blocks, attention regularization, and attention-based data augmentation. The developed framework is applicable to general CNN-based architectures and enhances decision making by paying specific attention to the discriminative regions of a white blood cell. The performance of the proposed method/model was evaluated through an extensive set of experiments and validation. The obtained results demonstrate the superior performance of the model achieving 99.69 % accuracy compared to other state-of-the-art approaches. The proposed model is a good alternative and complementary to existing computer diagnosis tools to assist pathologists in evaluating white blood cells from blood smear images." @default.
• W4308661657 created "2022-11-13" @default.
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• W4308661657 date "2022-10-21" @default.
• W4308661657 modified "2023-09-26" @default.
• W4308661657 title "White Blood Cell Classification Using Multi-Attention Data Augmentation and Regularization" @default.
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• W4308661657 doi "https://doi.org/10.3390/bdcc6040122" @default.
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