Abstract:

Accurate and efficient histopathological image classification of breast cancer is one of the important contents of computer-aided diagnosis. With the development of machine learning technology, deep learning has gradually become an effective method to classify breast cancer histopathological images. Firstly, the classification methods of breast cancer histopathological image and the existing problems are analyzed. Secondly, four relevant deep learning models are introduced, and the classification methods of breast cancer histopathological image based on deep learning are combed, and the performance of the existing models is compared and analyzed through experiments. Finally, the key issues of histopathological image classification of breast cancer are summarized and the future research trends are discussed.

Key words: computer aided diagnosis, breast cancer histopathological image, image classification, deep learning

摘要：

准确、高效的乳腺癌病理图像分类是计算机辅助诊断的重要研究内容之一。随着机器学习技术的发展，深度学习日渐成为一种有效的乳腺癌病理图像分类处理方法。分析了乳腺癌病理图像分类方法及目前存在的问题；介绍了四种相关的深度学习模型，对基于深度学习的乳腺癌病理图像分类方法进行梳理，并通过实验对比分析现有模型的性能；最后对乳腺癌病理图像分类的关键问题进行了总结，并讨论了未来研究的发展趋势。

关键词: 计算机辅助诊断, 乳腺癌病理图像, 图像分类, 深度学习