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LYON19- Lymphocyte Detection Test Set

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Zenodo2020-07-30 更新2026-05-25 收录
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<strong>LYON19</strong> The provided test set includes 441 ROIs saved in the .<em>png</em> files, and it is a test set of LYON grand challenge: https://lyon19.grand-challenge.org <strong>Data Description</strong> The test set contains Region of Interests (ROIs) selected from whole-slide images (WSI) of immunohistochemistry (IHC) stained specimens of breast, colon and prostate. Data came from eight different medical centers in the Netherlands. All slides were stained with an antibody against CD3 or CD8. Slides were subsequently digitized with a Pannoramic 250Flash II scanner (3DHistech, Hungary), resulting in WSIs with a spatial resolution of 0.24μm/px. Selected ROIs were saved with full resolution in the .<em>png</em> files. Selected ROIs were representative for most different types of lymphocyte distributions that occur in slides, namely (1) area with regular lymphocyte distribution, (2) clustered cells, and (3) staining or tissue artifacts. <strong>Citation:</strong> Please reference the following paper if you use LYON19 data for a scientific publication: Swiderska-Chadaj, Zaneta, et al. "<em><strong>Learning to detect lymphocytes in immunohistochemistry with deep learning</strong></em>." Medical Image Analysis (2019): 101547. Link to the paper: https://www.sciencedirect.com/science/article/pii/S1361841519300829

**LYON19数据集** 本测试集共包含441个感兴趣区域(Region of Interests, ROIs),全部以PNG格式存储,该数据集为LYON19大型挑战赛(LYON grand challenge)的官方测试集,赛事官网:https://lyon19.grand-challenge.org **数据说明** 本测试集的感兴趣区域提取自乳腺、结肠与前列腺组织的免疫组化(immunohistochemistry, IHC)染色标本的全切片图像(whole-slide images, WSIs)。数据采集自荷兰境内8家不同的医疗中心。所有切片均使用靶向CD3或CD8的抗体完成染色,随后通过Pannoramic 250Flash II扫描仪(匈牙利3DHistech公司出品)进行数字化扫描,生成的全切片图像空间分辨率为0.24μm/像素。所提取的ROIs均以全分辨率保存为PNG格式文件。 所选ROIs可覆盖切片中出现的绝大多数淋巴细胞分布类型,具体包括:(1) 淋巴细胞规则分布区域;(2) 细胞聚集团块;(3) 染色伪影或组织伪影。 **引用要求** 若您在学术发表中使用LYON19数据集,请引用以下文献:Swiderska-Chadaj, Zaneta, 等. 《基于深度学习实现免疫组化图像淋巴细胞检测》(*Learning to detect lymphocytes in immunohistochemistry with deep learning*). *Medical Image Analysis* (2019): 101547. 论文链接:https://www.sciencedirect.com/science/article/pii/S1361841519300829

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Zenodo
创建时间:
2019-09-05
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