For a favorable prognosis of breast cancer, early diagnosis is essential. The histopathological analysis is considered the gold standard to indicate the type of cancer. Histopathology consists of analyzing characteristics of the lesions through tissue sections stained with Hematoxylin and Eosin. During the last years, there is much interest in developing the histopathological slide analysis process. This article aims to explore recent literature related to intelligent analysis of breast cancer histopathological images, defining the taxonomy, identifying challenges, and open questions. The method is based on a systematic literature review, guided by research questions to identify relevant work and identify open problems in the literature. The present study investigates articles published in the last ten years. We are selecting and researching the most significant approaches according to pre-established criteria in the intelligent analysis of breast cancer histopathological images, resulting in a final corpus of 53 articles. As a result, we developed an updated taxonomy, identified the main challenges, public datasets, evaluation metrics, and techniques used in the studies. These results contribute to discussions about the intelligent analysis of breast cancer histopathological images and highlight some research gaps for future studies.

对于乳腺癌的良好预后，早期诊断是必不可少的。组织病理学分析被认为是指示癌症类型的金标准。组织病理学包括通过苏木精和伊红染色的组织切片分析病变的特征。在过去的几年里，人们对开发组织病理学幻灯片分析过程很感兴趣。本文旨在探索与乳腺癌组织病理学图像智能分析、定义分类、识别挑战和开放性问题相关的最新文献。该方法基于系统的文献综述，以研究问题为指导，以确定相关工作并确定文献中的开放性问题。本研究调查了过去十年发表的文章。我们正在根据乳腺癌组织病理学图像智能分析中的预先建立的标准选择和研究最重要的方法，最终形成 53 篇文章的语料库。因此，我们开发了更新的分类法，确定了研究中使用的主要挑战、公共数据集、评估指标和技术。这些结果有助于对乳腺癌组织病理学图像的智能分析的讨论，并突出了未来研究的一些研究空白。和研究中使用的技术。这些结果有助于对乳腺癌组织病理学图像的智能分析的讨论，并突出了未来研究的一些研究空白。和研究中使用的技术。这些结果有助于对乳腺癌组织病理学图像的智能分析的讨论，并突出了未来研究的一些研究空白。