Mendes Lauande Marcos Gabriel, Júnior Geraldo Braz, Sousa de Almeida João Dallyson, de Paiva Anselmo Cardoso, da Costa Rui Miguel Gil, Teles Amanda Mara, da Silva Leandro Lima, Brito Haissa Oliveira, Branco Vidal Flávia Castello
Applied Computing Group (NCA - UFMA) - Federal University of Maranhão - São Luís - MA, Brazil.
Postgraduate Program in Computer Science PPGCC - Federal University of Maranhão - São Luís - MA, Brazil.
Data Brief. 2025 Jun 21;61:111823. doi: 10.1016/j.dib.2025.111823. eCollection 2025 Aug.
Penile cancer has an incidence strongly linked to sociocultural factors, being more common in underdeveloped countries like Brazil, where it represents approximately 2% of cancers affecting men. This dataset was created to address the scarcity of publicly available resources for classifying histopathological images in penile cancer research. The images were collected in 2021 from tissue samples obtained through biopsies of patients undergoing treatment for penile cancer. After staining with Hematoxylin and Eosin (H&E), the tissue samples were photographed using a Leica ICC50 HD camera attached to a bright-field microscope (Leica DM500). The dataset comprises 194 high-resolution images (2048 × 1536 pixels), categorized by magnification (40X and 100X) and pathological classification (Tumor or Non-Tumor). Metadata includes additional information such as histological grade and, for some images, HPV status. Although previous works have focused primarily on binary classification tasks, the dataset includes additional labels, such as histological grade and HPV (Human Papilloma Virus) presence, which provide opportunities for multi-label classification or other types of predictive modelling. These extended labels enhance the dataset's versatility for more complex tasks in medical image analysis. The dataset holds significant reuse potential for machine learning tasks beyond binary classification, allowing researchers to explore additional layers of analysis, such as HPV detection and histological grading. It can also be used for model benchmarking and comparative studies in cancer research, contributing to developing new diagnostic tools. The dataset and metadata are available for further research and model development.
阴茎癌的发病率与社会文化因素密切相关，在巴西等不发达国家更为常见，在这些国家，阴茎癌约占男性癌症的2%。创建这个数据集是为了解决阴茎癌研究中用于组织病理学图像分类的公开可用资源稀缺的问题。这些图像于2021年从接受阴茎癌治疗的患者活检获得的组织样本中收集。在用苏木精和伊红（H&E）染色后，使用连接到明场显微镜（徕卡DM500）的徕卡ICC50 HD相机对组织样本进行拍照。该数据集包含194张高分辨率图像（2048×1536像素），按放大倍数（40倍和100倍）和病理分类（肿瘤或非肿瘤）进行分类。元数据包括其他信息，如组织学分级，对于一些图像，还包括HPV状态。尽管先前的工作主要集中在二元分类任务上，但该数据集包括额外的标签，如组织学分级和HPV（人乳头瘤病毒）的存在，这为多标签分类或其他类型的预测建模提供了机会。这些扩展标签增强了数据集在医学图像分析中用于更复杂任务的通用性。该数据集在二元分类之外的机器学习任务中具有显著的重用潜力，使研究人员能够探索额外的分析层面，如HPV检测和组织学分级。它还可用于癌症研究中的模型基准测试和比较研究，有助于开发新的诊断工具。该数据集和元数据可供进一步研究和模型开发使用。
