Augmenting Data by Learning Spatial and Appearance Transformations
Here are some slides I made to present this CVPR 2019 paper in our reading group:
Data Augmentation
GAN-based Synthetic Medical Image Augmentation
The paper proposes using Generative Adversarial Networks (GANs) to augment the dataset with high quality synthetic liver lesion images in order to improve the CNN classification performance for medical image classification. The authors use limited dataset of computed tomography (CT) images of 182 liver lesions (53 cysts, 64 metastases and 65 hemangiomas). The liver lesions vary considerably in shape, contrast and size, and also present intra-class variability. ...