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- W4313342086 abstract "Digital Pathology and examination of microscopy images is broadly used for the investigation of cell morphology or tissue structure. Manual assessment of the images is labour concentrated and inclined to inter-observer and intra-observer variations. The detection and segmentation of cell nuclei is the first step in quantitative analysis of biomedical microscopy images which helps in cancer diagnosis and prognosis. Many methods are available to segment nuclei in the images but a single method does not work on different imaging experiments. It needs to be chosen and designed for every experiment. Here we describe a deep learning approach for segmentation, that could be applied to different types of images and experimental conditions by not adjusting the parameters manually." @default.
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- W4313342086 date "2022-01-01" @default.
- W4313342086 modified "2023-10-10" @default.
- W4313342086 title "Nuclei Segmentation of Microscopic Images from Multiple Organs Using Deep Learning" @default.
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- W4313342086 doi "https://doi.org/10.1007/978-3-031-22405-8_23" @default.
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