Data Augmentation using CA Evolved GANs Mining medical data images have great potential for exploring hidden patterns in the medical domain. Medical data are heterogeneous which involves images to a great extent like MRI, ECG or Stroke effects etc. Knowledge discovery from such data can improve the diagnostic technique. However, to make the machine learn from such datasets requires large data. In the lowdata regime, machine learning algorithms work poorly. Data Augmentation alleviates this by using existing data more effectively, but standard data augmentation produces only limited alternative data. Recent developments in Deep Learning field is noteworthy when it comes to learning probability distribution of points through neural networks, and one of key part for such progress is because of Generative Adversarial Networks(GANs). In this paper, we propose an evolutionary training technique using a cultural algorithm(CA) for neuroevolution of deep task oriented GANs to find the best architecture for the given dataset. This architecture will help in generating similar but completely new data images which can be further used for training diagnostic Neural Networks. We have compared our approach with the Genetic Algorithm(GA) based neuroevolution of GANs and show that CA based neuroevolution of GANs evolves architecture which can generate a higher number of strokeface images with better resolution when there is low data of original stroke faces.

SemiSupervised AttentionGuided CycleGAN for Data Augmentation on Medical Images Recently, deep learning methods, in particular, convolutional neural networks (CNNs), have made a massive breakthrough in computer vision. And a big amount of annotated data is the essential cornerstone to reach this success. However, in the medical domain, it is usually difficult (and sometimes even impossible) to get sufficient data for some specific learning tasks. Consequently, in this work, a novel data augmentation solution, called semisupervised attentionguided CycleGAN (SSACycleGAN) is proposed to resolve this problem. Specifically, a cycleconsistency GANsbased model is first proposed to generate synthetic tumor (resp., normal) images from normal (resp., tumor) images. Then, a semisupervised attention module is further proposed to enhance the modelu0027s capability in learning the important details of the training images, which in turns help the generated synthetic images become more realistic. To verify its effectiveness, experimental studies are conducted on three medical image datasets with limited amounts of MRI images, and the proposed SSACycleGAN is applied to generate synthetic tumor and normal MRI images for data augmentation. Experimental results show that i) SSACycleGAN can add (resp., remove) tumor lesions on (resp., from) the original normal (resp., tumor) images and generate very realistic synthetic tumor (resp. normal) images; and ii) in the ResNet18based MRI image classification tasks based on these datasets, data augmentation using SSACycleGAN achieves much better classification performances than the classic data augmentation methods.