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- W3208586508 abstract "Automatic segmentation of bone marrow cells plays an important role in the diagnosis of many blood diseases such as anemia and leukemia. Due to the complex morphology and wide variety of bone marrow cells, their segmentation is still a challenging task. To improve the accuracy of bone marrow cell segmentation, we propose an end-to-end U-shaped network based on the pyramid residual convolution and the attention mechanism. Specifically, the standard convolution and dilated convolution are combined as its feature encoder, which designs a pyramid residual convolution block to extract multi-scale features. Then, the attention gating mechanism is introduced into each skip connection module for fusing the shallow and deep information. Finally, the proposed method combines convolution and deconvolution for feature decoding to achieve the final segmentation of bone marrow cells. Experiments with quantitative and qualitative comparisons are carried out on a self-built bone marrow smear dataset. Experimental results demonstrate the superiority of the proposed method over several state-of-the-art methods." @default.
- W3208586508 created "2021-11-08" @default.
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- W3208586508 date "2021-01-01" @default.
- W3208586508 modified "2023-09-27" @default.
- W3208586508 title "Bone Marrow Cell Segmentation Based on Improved U-Net" @default.
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- W3208586508 doi "https://doi.org/10.1007/978-981-16-7476-1_9" @default.
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