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• W4285293238 abstract "Accurate segmentation and detection (instance segmentation) of white blood cells (WBCs) from whole slide images remains a challenging task, as the WBCs vary widely in shapes, sizes, and colors caused by different cell subtypes and various staining techniques. In this paper, we propose a novel framework for end-to-end segmentation and detection of WBCs that are on multiple scales and stained by different techniques. We name the framework the multi-scale and multi-staining WBC instance segmentation network (MSS-WISN). The MSS-WISN consists of two parts: 1) a feature extraction network for strengthening the feature expression and minimizing the impact of different staining techniques, and 2) a feature fusion network for highlighting salient features and thereby eliminating the effect of scale variations. To verify the effectiveness of the MSS-WISN, we build a new dataset containing 302 Magenta stained images (collected by Tianjin Medical University) and 242 Wright stained images (from a public dataset). Experiments show that the proposed framework outperforms other state-of-the-art methods in terms of WBC detection and WBC segmentation." @default.
• W4285293238 created "2022-07-14" @default.
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• W4285293238 date "2022-01-01" @default.
• W4285293238 modified "2023-10-14" @default.
• W4285293238 title "MSS-WISN: Multiscale Multistaining WBCs Instance Segmentation Network" @default.
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• W4285293238 doi "https://doi.org/10.1109/access.2022.3182800" @default.
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