泛肿瘤CAnine cuTaneous癌症组织学（CATCH）数据集
Pan-tumor CAnine cuTaneous Cancer Histology (CATCH) dataset
摘要：
由于形态学的相似性，皮肤肿瘤的组织学切片分化为单个亚型可能具有挑战性。最近，基于深度学习的方法已经证明了它们在这方面支持病理学家的潜力。然而，许多这些监督算法需要大量的注释数据进行健壮的开发。我们提供了一个公开的数据集，包含7种不同犬皮肤肿瘤的350张全切片图像，并补充了13种组织学类别的12，424个多边形注释，包括7种皮肤肿瘤亚型。在评分者间实验中，我们显示了所提供的标签的高度一致性，特别是对于肿瘤注释。我们通过训练用于组织分割和肿瘤亚型分类任务的深度神经网络来进一步验证数据集。我们实现了类平均Jaccard系数为0.7047，特别是肿瘤为0.9044。对于分类，我们实现了0.9857的幻灯片级精度。由于犬皮肤肿瘤与人类肿瘤具有各种组织学同源性，因此该数据集的附加值不仅限于兽医病理学，而且扩展到更广泛的应用领域。
英文摘要：
Due to morphological similarities, the differentiation of histologic sections of cutaneous tumors into individual subtypes can be challenging. Recently, deep learning-based approaches have proven their potential for supporting pathologists in this regard. However, many of these supervised algorithms require a large amount of annotated data for robust development. We present a publicly available dataset of 350 whole slide images of seven different canine cutaneous tumors complemented by 12,424 polygon annotations for 13 histologic classes, including seven cutaneous tumor subtypes. In inter-rater experiments, we show a high consistency of the provided labels, especially for tumor annotations. We further validate the dataset by training a deep neural network for the task of tissue segmentation and tumor subtype classification. We achieve a class-averaged Jaccard coefficient of 0.7047, and 0.9044 for tumor in particular. For classification, we achieve a slide-level accuracy of 0.9857. Since canine cutaneous tumors possess various histologic homologies to human tumors the added value of this dataset is not limited to veterinary pathology but extends to more general fields of application.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
