CR-AI4SkIN dataset
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CR-AI4SkIN is a public dataset composed of H&E patches extracted from Whole Slide Images. This dataset contains Cutaneous Spindle Cell neoplasms, and the task is to classify WSIs into benign or malignant. The main feature of this dataset is that it has been labeled by ten non-expert annotators. This repository contains the associated files to replicate the study titled "Annotation Protocol and Crowdsourcing Multiple Instance Learning Classification of Skin Histological Images: The CR-AI4SkIN Dataset", published in the Artificial Intelligence in Medicine journal. For further details on the study and the dataset, please see the published article. The zipped file 'annotations.zip' contains three .csv files with crowdsourcing annotations. These files provide the train/val/test split as well as label information. The 'GT' column stands for the expert label, 'MV' for the majority vote among non-experts, and 'Marker_X' is the label given by the X-th annotator. The zipped file 'img.zip' contains the dataset images divided into two sub-directories indicating the source hospital. Each image is associated with its own folder, which is identified using anonymized IDs. Within each folder, we include the extracted patches representing the predicted regions of interest. Note: We did not include 1 image in the training set and another in the test set because the regions of interest were not found/had troubles. Label Dictionary: 0: Benign 1: Malignant -1: Missing value (e.g., if an annotator did not label that image). Hospital Dictionary: HCUV: Hospital Clínico Universitario de Valencia (Valencia Hospital). HUSC: Hospital Universitario San Cecilio (Granada Hospital). Citation: If you use this dataset, please cite the following article: @article{del2023annotation, title={Annotation protocol and crowdsourcing multiple instance learning classification of skin histological images: The CR-AI4SkIN dataset}, author={Del Amor, Rocío and Pérez-Cano, Jose and López-Pérez, Miguel and Terradez, Liria and Aneiros-Fernandez, Jose and Morales, Sandra and Mateos, Javier and Molina, Rafael and Naranjo, Valery}, journal={Artificial Intelligence in Medicine}, volume={145}, pages={102686}, year={2023}, publisher={Elsevier} } Funding: This work has received funding from the Spanish Ministry of Economy and Competitiveness through project PID2019-105142RB (AI4SKIN) and Spanish Ministry of Science and Innovation through project PID2022-140189OB, from Horizon 2020, the European Union’s Framework Programme for Research and Innovation, under the grant agreement No. 860627 (CLARIFY), grant B-TIC-324-UGR20 funded by Consejería de Universidad, Investigación e Innovación (Junta de Andalucía) and by “ERDF A way of making Europe”, and GVA through the project INNEST/2021/321 (SAMUEL). The work of Rocío del Amor has been supported by the Spanish Ministry of Universities (FPU20/05263). The work of Miguel López Pérez has been supported by the University of Granada postdoctoral program “Contrato Puente”. The work of Sandra Morales has been co-funded by the Universitat Politècnica de València through the program PAID-10-20.
CR-AI4SkIN是一个公开数据集，其样本源自全切片图像（Whole Slide Image, WSI）中提取的苏木精-伊红（Hematoxylin and Eosin, H&E）染色图像块。该数据集涵盖皮肤梭形细胞肿瘤（Cutaneous Spindle Cell Neoplasms），核心任务为将全切片图像分类为良性与恶性两类。本数据集的显著特征在于，其标注工作由10名非专业标注人员完成。 本仓库包含复现论文《Annotation Protocol and Crowdsourcing Multiple Instance Learning Classification of Skin Histological Images: The CR-AI4SkIN Dataset》所需的配套文件，该论文已发表于《Artificial Intelligence in Medicine》期刊。如需了解该研究与数据集的更多细节，请查阅正式发表的学术论文。 压缩文件`annotations.zip`内含3个逗号分隔值（Comma-Separated Values, CSV）格式的众包标注文件，这些文件提供了训练集、验证集与测试集的划分方案以及标签信息。其中，`GT`列代表专家标注标签，`MV`列代表非专业标注人员的多数投票标签，`Marker_X`则表示第X位标注人员给出的个体标注结果。 压缩文件`img.zip`包含数据集图像，其按照图像来源医院划分为两个子目录。每张图像对应一个以匿名ID命名的专属文件夹，文件夹内包含代表预测感兴趣区域的提取图像块。注意：由于未找到感兴趣区域或存在相关问题，训练集与测试集各有1张图像未被纳入本数据集。 标签对照表如下：0表示良性，1表示恶性，-1表示缺失值（例如当标注人员未对该图像进行标注时）。 医院对照表如下：HCUV即瓦伦西亚大学临床医院（Hospital Clínico Universitario de Valencia），HUSC即格拉纳达圣塞西利奥大学医院（Hospital Universitario San Cecilio）。 引用说明：若您使用本数据集，请引用以下学术论文： @article{del2023annotation, title={Annotation protocol and crowdsourcing multiple instance learning classification of skin histological images: The CR-AI4SkIN dataset}, author={Del Amor, Rocío and Pérez-Cano, Jose and López-Pérez, Miguel and Terradez, Liria and Aneiros-Fernandez, Jose and Morales, Sandra and Mateos, Javier and Molina, Rafael and Naranjo, Valery}, journal={Artificial Intelligence in Medicine}, volume={145}, pages={102686}, year={2023}, publisher={Elsevier} } 资助说明：本研究获得西班牙经济与竞争力部通过项目PID2019-105142RB（AI4SKIN）、西班牙科学与创新部通过项目PID2022-140189OB的资助；同时获欧盟研究与创新框架计划“地平线2020（Horizon 2020）”下编号为860627的CLARIFY项目资助，以及安达卢西亚自治区大学、研究与创新委员会资助的项目B-TIC-324-UGR20（该项目同时获得“欧洲区域发展基金：打造欧洲之路”支持），另获巴伦西亚自治区通过项目INNEST/2021/321（SAMUEL）提供的资助。 Rocío del Amor的研究工作得到西班牙大学部（项目编号FPU20/05263）的支持；Miguel López Pérez的研究工作获得格拉纳达大学“Contrato Puente”（桥梁合同）博士后项目支持；Sandra Morales的研究工作由瓦伦西亚理工大学PAID-10-20项目联合资助。
