The automatic classification of breast cancer pathological images has important clinical application value. However, to develop the classification algorithm using the artificially extracted image features faces several challenges including the requirement of professional domain knowledge to extractand compute highiquality image features, which are often time-consuming, laborious, and difficult. For overcoming these challenges, this study developed and applied an improved deep convolutional neural network model to perform automatic classification of breast cancer using pathological images. Specifically, in this study, data enhancement and migration learning methods are used to effectively avoid the overfitting problems with deep learning models when they are limited by training image sample size. Experimental results show that a 91% recognition rate or accuracy when applying this improved deep learning model to a publicly available dataset of BreaKHis. Comparing with other previously used models, the new model yields good robustness and generalization.

中文翻译：

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基于深度学习的乳腺癌病理图像分类[J].

乳腺癌病理图像的自动分类具有重要的临床应用价值。然而，使用人工提取的图像特征来开发分类算法面临着几个挑战，包括需要专业领域知识来提取和计算高质量的图像特征，这通常是耗时、费力和困难的。为了克服这些挑战，本研究开发并应用了改进的深度卷积神经网络模型，以使用病理图像对乳腺癌进行自动分类。具体而言，在本研究中，数据增强和迁移学习方法被用于有效避免深度学习模型在受训练图像样本大小限制时的过拟合问题。实验结果表明，将这种改进的深度学习模型应用于 BreaKHis 的公开数据集时，识别率或准确率达到 91%。与之前使用的其他模型相比，新模型具有良好的鲁棒性和泛化性。