Digital pathology represents a major evolution in modern medicine. Pathological examinations constitute the standard in medical protocols and the law, and call for specific action in the diagnostic process. Advances in digital pathology have made it possible for image analysis to take advantage of the information analysis from hematoxylin and eosin stained images. In spite of concern, it is recorded in the majority of breast cancer datasets, which makes research more difficult in prediction. The objective of our work is to evaluate the performance of the machine learning and deep learning techniques applied to predict breast cancer recurrence rates. This study starts with an overview of tissue preparation, analysis of stained images, and a prognosis for cancer patients. The high accuracy results recorded are compromised in terms of sensitivity and specificity. The missing loss function and class imbalance problems are rarely addressed, and most often the chosen performance measures are context-inappropriate. The challenge that presents itself is to analyse whole slide images for the content imaging required with diagnostic biomarkers, and prognosis support backed by digital pathology.

数字病理学代表了现代医学的重大发展。病理检查构成医学规程和法律的标准，并要求在诊断过程中采取特定措施。数字病理学的进步使图像分析能够利用苏木精和曙红染色图像的信息分析优势。尽管令人担忧，但大多数乳腺癌数据集都记录了该信息，这使得研究更加难以预测。我们工作的目的是评估用于预测乳腺癌复发率的机器学习和深度学习技术的性能。这项研究首先概述了组织准备，染色图像分析以及癌症患者的预后。记录的高精度结果在灵敏度和特异性方面受到损害。丢失的损失函数和类不平衡问题很少得到解决，并且大多数情况下，所选择的绩效指标不适合上下文。自身面临的挑战是分析整个幻灯片图像以进行诊断生物标记物所需的内容成像，并获得数字病理学支持的预后支持。