Aksac Alper, Demetrick Douglas J, Ozyer Tansel, Alhajj Reda
Department of Computer Science, University of Calgary, Calgary, AB, T2N 1N4, Canada.
Department of Pathology & Laboratory Medicine, University of Calgary and Calgary Laboratory Services, Calgary, AB, T2L 2K8, Canada.
BMC Res Notes. 2019 Feb 12;12(1):82. doi: 10.1186/s13104-019-4121-7.
Histopathological tissue analysis by a pathologist determines the diagnosis and prognosis of most tumors, such as breast cancer. To estimate the aggressiveness of cancer, a pathologist evaluates the microscopic appearance of a biopsied tissue sample based on morphological features which have been correlated with patient outcome.
This paper introduces a dataset of 162 breast cancer histopathology images, namely the breast cancer histopathological annotation and diagnosis dataset (BreCaHAD) which allows researchers to optimize and evaluate the usefulness of their proposed methods. The dataset includes various malignant cases. The task associated with this dataset is to automatically classify histological structures in these hematoxylin and eosin (H&E) stained images into six classes, namely mitosis, apoptosis, tumor nuclei, non-tumor nuclei, tubule, and non-tubule. By providing this dataset to the biomedical imaging community, we hope to encourage researchers in computer vision, machine learning and medical fields to contribute and develop methods/tools for automatic detection and diagnosis of cancerous regions in breast cancer histology images.
病理学家进行的组织病理学分析可确定大多数肿瘤（如乳腺癌）的诊断和预后。为评估癌症的侵袭性，病理学家会根据与患者预后相关的形态学特征，评估活检组织样本的微观外观。
本文介绍了一个包含162张乳腺癌组织病理学图像的数据集，即乳腺癌组织病理学标注与诊断数据集（BreCaHAD），该数据集可让研究人员优化并评估他们所提出方法的有效性。该数据集包括各种恶性病例。与这个数据集相关的任务是将这些苏木精和伊红（H&E）染色图像中的组织结构自动分类为六个类别，即有丝分裂、凋亡、肿瘤细胞核、非肿瘤细胞核、小管和非小管。通过向生物医学成像领域提供这个数据集，我们希望鼓励计算机视觉、机器学习和医学领域的研究人员为乳腺癌组织学图像中癌区域的自动检测和诊断贡献并开发方法/工具。
