PanNuke癌组织细胞数据集

半自动生成的细胞核实例分割和分类数据集，包含 19 种不同组织类型的详尽细胞核标签。该数据集由 481 个视野组成，其中 312 个视野是从多个数据源的 20K 多个不同放大倍率的整张幻灯片图像中随机采样的。该数据集总共包含 205,343 个标记的核，每个核都有一个实例分割掩码。在 pannuke 上训练的模型可以帮助整个幻灯片图像组织类型分割，并推广到新组织。PanNuke 演示了首批成功半自动生成的数据集之一。

Publications

				@inproceedings{gamper2019pannuke,
 title={PanNuke: an open pan-cancer histology dataset for nuclei instance segmentation and classification},
 author={Gamper, Jevgenij and Koohbanani, Navid Alemi and Benes, Ksenija and Khuram, Ali and Rajpoot, Nasir},
 booktitle={European Congress on Digital Pathology},
 pages={11--19},
 year={2019},
 organization={Springer}
}
			

				@article{gamper2020pannuke,
 title={PanNuke Dataset Extension, Insights and Baselines},
 author={Gamper, Jevgenij and Koohbanani, Navid Alemi and Graham, Simon and Jahanifar, Mostafa and Khurram, Syed Ali and Azam, Ayesha and Hewitt, Katherine and Rajpoot, Nasir},
 journal={arXiv preprint arXiv:2003.10778},
 year={2020}
}
			

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University of Warwick website

PanNuke is a semi automatically generated nuclei instance segmentation and classification dataset with exhaustive nuclei labels across 19 different tissue types. The dataset consists of 481 visual fields, of which 312 are randomly sampled from more than 20K whole slide images at different magnifications, from multiple data sources. In total the dataset contains 205,343 labeled nuclei, each with an instance segmentation mask.

平台的 数据集版块，共有192个不同类别，不同应用的数据集。本周在此基础上，又上新 8种人体姿态识别相关的数据集，目前总共有200种数据集。 ① 关键点定位 （1）头颅Cephalometric X-Ray数据集 数据集图片： 数据…

PanNuke - Largest PanCancer Dataset for Histology Nuclei Instance Segmentation and Classification

The NuCLS dataset contains over 220,000 labeled nuclei from breast cancer images from TCGA. These nuclei were annotated through the collaborative effort of pathologists, pathology residents, and medical students using the Digital Slide Archive. These data can be used in several ways to develop and validate algorithms for nuclear detection, classification, and segmentation, or as a resource to develop and evaluate methods for interrater analysis.

Data from both single-rater and multi-rater studies are provided. For single-rater data we provide both pathologist-reviewed and uncorrected annotations. For multi-rater datasets we provide annotations generated with and without suggestions from weak segmentation and classification algorithms.

现如今，随着人们生活方式和环境的改变，恶性肿瘤已经成为疾病死亡病因之一。肿瘤在全球呈现发病率增高，以及发病年龄年轻化的趋势。 2019年 ， A Cancer Journal For Clinicians 杂志发布了最新的数据。 该报告估…

三重u-net:具有渐进式密集特征聚集的苏木精感知的细胞核分割摘要核分割是病理性肿瘤研究的重要环节。由于手工操作不均匀导致的颜色不一致、肿瘤细胞核边界模糊、肿瘤细胞重叠等困难，目前仍是一个有待解决的问题。在本文中，我们的目的是利用H&E染色图像的独特的外观特征，即苏木精总是将细胞核染成蓝色，而伊红总是将细胞外基质和细胞质染成粉红色。因此，我们利用比尔-朗伯定律从RGB图像中提取苏木精成分。根据光学属性，提取的Hematoxylin组分对颜色不一致具有较强的鲁棒性。利用苏木

乳腺癌细胞图像数据集、血细胞图像数据集、HE染色切片、疟疾细胞图像图像识别、分类、分割

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