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A region-based convolutional network for nuclei detection and segmentation in microscopy images

https://doi.org/10.1016/j.bspc.2021.103276

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What this work says

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inferred← citeshttps://doi.org/10.1007/s11517-024-03050-xopenalex-snapshot

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inferred← citeshttps://doi.org/10.1016/j.bspc.2023.105544openalex-snapshot

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inferred← citeshttps://doi.org/10.1016/j.bspc.2024.107140openalex-snapshot

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inferred← citeshttps://doi.org/10.1016/j.bspc.2025.108724openalex-snapshot

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inferred← citeshttps://doi.org/10.1016/j.eswa.2024.124309openalex-snapshot

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inferred← citeshttps://doi.org/10.1016/j.media.2023.102790openalex-snapshot

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inferred← citeshttps://doi.org/10.1016/j.neucom.2024.127445openalex-snapshot

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inferred← citeshttps://doi.org/10.1049/cit2.12351openalex-snapshot

WaveSeg‐UNet model for overlapped nuclei segmentation from multi‐organ histopathology images

inferred← citeshttps://doi.org/10.1049/ipr2.70129openalex-snapshot

A Systematic Review on Cell Nucleus Instance Segmentation

inferred← citeshttps://doi.org/10.1108/atsip-06-2025-0057openalex-snapshot

LG-NuSegHop: A Local-to-global Self-supervised Pipeline for Nuclei Instance Segmentation

inferred← citeshttps://doi.org/10.1109/apsipaasc63619.2025.10848765openalex-snapshot

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inferred← citeshttps://doi.org/10.1109/iccv51070.2023.01955openalex-snapshot

Affine-Consistent Transformer for Multi-Class Cell Nuclei Detection

inferred← citeshttps://doi.org/10.1109/medai59581.2023.00023openalex-snapshot

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inferred← citeshttps://doi.org/10.1109/tmi.2025.3645860openalex-snapshot

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Deep Learning and Computer Vision: Machine Learning Analysis and Image Processing of Puromycin Treated Microscopy

inferred← citeshttps://doi.org/10.3390/jimaging10120311openalex-snapshot

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