结直肠癌（CRC）目前是最常见且致命的癌症之一。CRC是世界第三常见的恶性肿瘤和第四大致癌死因。它在美国和其他发达国家被认为是导致癌症相关死亡的第二大原因。组织病理学图像包含足够的表型信息，在CRC的诊断和治疗中起着不可或缺的作用。为了提高胃肠组织病理学图像分析的客观性和诊断效率，基于机器学习（ML）的计算机辅助诊断（CAD）方法在胃肠组织病理学图像分析中被广泛应用。在本研究中，我们对近年来基于ML的胃肠组织病理学图像分析方法进行全面研究。首先，我们讨论了从基础研究中获取的具有医学相关性的胃肠组织病理学常用数据集。其次，我们介绍了常用于胃肠组织病理学的传统ML方法，以及深度学习（DL）方法。然后，我们全面回顾了ML方法在肠道组织病理学图像的分割、分类、检测和识别等方面的最新进展。最后，我们对现有方法进行了研究，并给出了这些方法在该领域的应用前景。版权所有©2023 Elsevier Ltd.保留所有权利。

Colorectal Cancer (CRC) is currently one of the most common and deadly cancers. CRC is the third most common malignancy and the fourth leading cause of cancer death worldwide. It ranks as the second most frequent cause of cancer-related deaths in the United States and other developed countries. Histopathological images contain sufficient phenotypic information, they play an indispensable role in the diagnosis and treatment of CRC. In order to improve the objectivity and diagnostic efficiency for image analysis of intestinal histopathology, Computer-aided Diagnosis (CAD) methods based on machine learning (ML) are widely applied in image analysis of intestinal histopathology. In this investigation, we conduct a comprehensive study on recent ML-based methods for image analysis of intestinal histopathology. First, we discuss commonly used datasets from basic research studies with knowledge of intestinal histopathology relevant to medicine. Second, we introduce traditional ML methods commonly used in intestinal histopathology, as well as deep learning (DL) methods. Then, we provide a comprehensive review of the recent developments in ML methods for segmentation, classification, detection, and recognition, among others, for histopathological images of the intestine. Finally, the existing methods have been studied, and the application prospects of these methods in this field are given.Copyright © 2023 Elsevier Ltd. All rights reserved.