Annu Int Conf IEEE Eng Med Biol Soc. 2021 Nov;2021:3387-3390. doi: 10.1109/EMBC46164.2021.9630678.
Data limitation is one of the major challenges in applying deep learning to medical images. Data augmentation is a critical step to train robust and accurate deep learning models for medical images. In this research, we increase the size of a small dataset by using an Auxiliary Classifier Generative Adversarial Network (ACGAN) which generates realistic images along with their class labels.We evaluate the effectiveness of our ACGAN augmentation method by performing breast cancer histopathological image classification with deep convolutional neural network (dCNN) classifiers trained on our enhanced dataset. For our classifier, we use a transfer learning approach where the convolutional features are extracted from a pertained model and subsequently fed into several extreme gradient boosting (XGBoost) classifiers. Our experimental results on Breast Cancer Histopathological (BreakHis) dataset show that ACGAN data augmentation, along with our XGBoost classifier increases the classification accuracy by 9.35% for binary classification (benign vs. malignant) and 8.88% for four-class tumor sub-type classification compared with standard transfer learning approach.
数据限制是将深度学习应用于医学图像的主要挑战之一。数据增强是训练用于医学图像的强大而准确的深度学习模型的关键步骤。在这项研究中，我们使用辅助分类器生成对抗网络（ACGAN）来增加小数据集的大小，该网络可以生成具有类标签的逼真图像。我们通过使用在增强的数据集上训练的深度卷积神经网络（dCNN）分类器对乳腺癌组织病理学图像进行分类，来评估我们的 ACGAN 增强方法的有效性。对于我们的分类器，我们使用迁移学习方法，从预先训练的模型中提取卷积特征，然后将其输入到几个极端梯度提升（XGBoost）分类器中。我们在乳腺癌组织病理学（BreakHis）数据集上的实验结果表明，与标准的迁移学习方法相比，ACGAN 数据增强以及我们的 XGBoost 分类器将二进制分类（良性与恶性）的分类准确性提高了 9.35%，将四类肿瘤亚型分类的准确性提高了 8.88%。
