Breast Cancer Histopathological Image Classification with Adversarial Image Synthesis

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 935% for binary classification (benign vs. malignant) and 8.88% for four-class tumor sub-type classification compared with standard transfer learning approach.